Paper deep dive

PhysEvo: Astra Can Act, Let It

Wenqing Tian, Zeyu Zhang, Zhaocheng Liu, Fengwei Liu, Qiang Liu, Liang Wang

Year: 2026Venue: arXiv preprintArea: cs.ROType: PreprintEmbeddings: 74

Intelligence

Status: succeeded | Model: Gemma-4-26B-A4B | Prompt: intel-v1 | Confidence: 93%

Last extracted: 10/8/2026, 6:38:58 AM

Summary

PhysEvo is a framework for physical recursive self-improvement (RSI) using a single frozen Astra model. It employs a task agent for execution and a meta-agent for diagnosis, revision, and testing of tools and skills without updating model weights. PhysEvo significantly outperforms direct Astra and other baselines like RoboDawn and Simate-beta on RoboDojo simulation tasks and real-world hardware (AgileX PiPER).

Entities (9)

PhysEvo · framework · 100%Astra · model · 98%AgileX PiPER · robothardware · 95%Recursive self-improvement · concept · 95%RoboDojo · benchmark · 95%Meta-Agent · component · 90%RoboDawn · baseline · 90%Simate-beta · baseline · 90%Task Agent · component · 90%

Relation Signals (7)

PhysEvo → deployedon → AgileX PiPER

confidence 95% · Deploying the simulation-evolved harness on AgileX PiPER and continuing skill revision yields 90.60/100 average score

PhysEvo → evaluatedon → RoboDojo

confidence 95% · Across 42 RoboDojo tasks, held-out-layout evaluation of retained task-specific deployment versions yields a five-dimension average score of 68.14/100

PhysEvo → uses → Astra

confidence 95% · We introduce PhysEvo, a framework for physical recursive self-improvement (RSI) around a single frozen model.

PhysEvo → outperforms → RoboDawn

confidence 90% · PhysEvo yields a five-dimension average score of 68.14/100 and 62.00% success, compared with 47.17% for RoboDawn's one-shot Astra agent

PhysEvo → outperforms → Simate-beta

confidence 90% · On eight manipulation tasks challenging direct Astra, PhysEvo achieves 55.00% success, compared with 50.83% for Simate-beta

Meta-Agent → partof → PhysEvo

confidence 90% · A task agent executes robot tasks; a meta-agent uses the resulting trajectories to diagnose failures, revise tools and skills, and test corrections.

Task Agent → partof → PhysEvo

confidence 90% · A task agent executes robot tasks; a meta-agent uses the resulting trajectories to diagnose failures, revise tools and skills, and test corrections.

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Abstract

Abstract:Astra can act, yet reliable manipulation depends on the system through which it observes and controls the world. We introduce PhysEvo, a framework for physical recursive self-improvement (RSI) around a single frozen model. A task agent executes robot tasks; a meta-agent uses the resulting trajectories to diagnose failures, revise tools and skills, and test corrections. The meta-agent can also improve its own diagnostic tools, so retained revisions support both later action and later self-improvement. This process develops joint-level control, evidence-seeking observation, and reusable manipulation skills without model-weight updates or a separately trained action policy. Across 42 RoboDojo tasks, held-out-layout evaluation of retained task-specific deployment versions yields a five-dimension average score of 68.14/100 and 62.00% success, compared with 47.17% for RoboDawn's one-shot Astra agent, the strongest published reference in our comparison. On eight manipulation tasks challenging direct Astra, PhysEvo achieves 55.00% success, compared with 1.25% for the direct-Astra reference. Deploying the simulation-evolved harness on AgileX PiPER and continuing skill revision yields 90.60/100 average score and 84.00% success across 25 trials on five real-world tasks. PhysEvo turns the consequences of action into persistent, testable changes to how a frozen model acts and improves.

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PhysEvo: Astra Can Act, Let It Wenqing Tian 1,2 Zeyu Zhang 1,2 Zhaocheng Liu 4,†,‡ Fengwei Liu 3 Qiang Liu 1,2,† Liang Wang 1,2 1 University of Chinese Academy of Sciences 2 Institute of Automation, Chinese Academy of Sciences 3 Tsinghua University 4 Independent Researcher lio.h.zen@gmail.com qiang.liu@nlpr.ia.ac.cn ABSTRACT Astra can act, yet reliable manipulation depends on the system through which it observes and controls the world. We introduce PhysEvo, a framework for physical recursive self-improvement (RSI) around a single frozen model. A task agent executes robot tasks; a meta-agent uses the resulting trajectories to diagnose failures, revise tools and skills, and test corrections. The meta-agent can also improve its own diagnostic tools, so retained revisions support both later action and later self-improvement. This process develops joint-level control, evidence-seeking observation, and reusable manipulation skills without model-weight updates or a separately trained action policy. Across 42 RoboDojo tasks, held-out-layout evaluation of retained task-specific deployment versions yields a five-dimension average score of 68.14/100 and 62.00% success, compared with 47.17% for RoboDawn’s one-shot Astra agent, the strongest published reference in our comparison. On eight manipulation tasks challenging direct Astra, PhysEvo achieves 55.00% success, compared with 1.25% for the direct-Astra reference. Deploying the simulation-evolved harness on AgileX PiPER and continuing skill revision yields 90.60/100 average score and 84.00% success across 25 trials on five real-world tasks. PhysEvo turns the consequences of action into persistent, testable changes to how a frozen model acts and improves. Act Execute with current harness Diagnose Analyze Execution evidence Revise Edit task & meta harness Validate Test candidate revisions Retain & Redeploy Embodied self-evolution through execution feedback From attempts to progress Better embodied agents through experience Stack bowls Unstacked bowls Make toast Missed slots Classify by language Return blocked Hang mugs Cup slipping Stack bowls Bowls stacked Make toast Bread inserted Classify by language Return completed Hang mugs Stable grasp Limited evidence Restricted control Misleading feedback Richer evidence Expanded control Reliable feedback Eight challenging tasks for Direct Astra Direct AstraPhysEvo 1.25% → 55.00% +53.75 percentage points Direct Astra GalaxeaVLA G0.5 RoboDawn (1-shot) Liber-0 Preview Simate-beta PhysEvo 0102030405060 1.25 43.09 45.00 46.44 50.83 55.00 Mean success rate (%) Figure 1: Physical RSI in PhysEvo: execute, diagnose, revise, test, and inherit. The lower panel compares success on eight challenging tasks for Direct Astra (table 3). † Corresponding authors. ‡ Project leader. 1 arXiv:2610.08995v1 [cs.RO] 6 Oct 2026 1INTRODUCTION Astra can already control a robot, but demonstrated competence does not guarantee reliable execution. In a ten-task RoboDojo evaluation, direct Astra reaches 100% success on object classification yet succeeds on only 26% of attempts overall. Pairing it with the specialized vision–language–action (VLA) policyπ 0.5 raises success to 48% (Su et al., 2026). These results pose a question: what prevents capabilities that are already present from being used reliably? Our executions expose bottlenecks in the harness: the tools, skills, and execution logic between the model and the robot. During pouring, the held bottle obscures the wrist view needed to monitor liquid delivery. With a tool-center-point (TCP) interface, local inverse kinematics (IK) can fold the arm into an obstructive posture, while leaving Astra no explicit way to choose the joint configuration (figures 3 and 4). These failures call for more than a new action through an unchanged interface: the agent needs a useful view or direct authority over posture. Why should the same model be able to fix a failure it did not prevent? Retrospective diagnosis has access to evidence that was unavailable during action selection. A trajectory reveals the consequences of a command; aligned images, robot states, and code help trace those consequences to a cause. Astra can then propose a concrete intervention, such as maintaining a free-arm side view or exposing joint reconfiguration, and test it through further execution. In our pouring case, revised skill guidance changes the observation strategy, and the subsequent episode succeeds. Physical interaction thus provides both evidence for a correction and a test of whether it works. A correction need not end with the episode. Retained tools and skills change later execution; retained diagnostic tools change how later failures can be investigated. In our packing case, Astra reuses a review tool it built earlier to trace box displacement to preceding contact and revise clearance guidance (figure 5). We call this loop physical recursive self-improvement (RSI): experience revises the system used for action and diagnosis, and the revised system is inherited by subsequent rounds. We introduce PhysEvo, which implements this loop with one frozen Astra in two roles. The task agent observes and controls the robot. The meta-agent inspects execution, edits tools and skills, tests revisions, and retains useful changes, including changes to its own diagnostic resources. The resulting framework combines joint-level control, active and historical observation, geometry tools, and manipulation skills. Across 42 RoboDojo tasks, held-out-layout evaluation of retained task-specific deployment versions achieves 68.14/100 score and 62.00% success under equal five-dimension weighting. This exceeds the 47.17% reported by RoboDawn’s one-shot Astra agent, the strongest reference in our comparison (table 1) (Guo et al., 2026a). On the ten-task comparison with direct and hybrid policies, PhysEvo reaches 68.00% success versus 26.00% and 48.00%, respectively. On eight manipulation tasks challenging direct Astra, it reaches 55.00% versus 1.25%, a 53.75-point gain; Simate-beta, the strongest robot-policy reference on this subset, reports 50.83% (figure 1 and table 3) (RoboDojo Team, 2026; Zhang et al., 2026d). On AgileX PiPER, the simulation-evolved harness serves as a starting point for continued adaptation. Across five trials on each of five real-world tasks, PhysEvo achieves a mean score of 90.60/100 and 84.00% success. Revisions compensate for liquid-flow effects and replace geometry-first localization with a visually guided approach followed by close-range geometric refinement (section 5.6). The same loop can therefore revise both how the robot moves and when it seeks geometric evidence. 2 Our contributions are (i) a physical RSI framework in which one frozen model acts, revises, and inherits task and meta resources; (i) an evolved interaction architecture combining joint control, evidence-seeking observation, and reusable skills; and (i) a 42-task evaluation with mechanism-focused cases and a five-task evaluation of continued adaptation on real hardware. 2RELATED WORK Foundation models for robot control. Vision–language–action models such as RT-2, OpenVLA, and π 0.5 learn control from robot data (Brohan et al., 2023; Kim et al., 2024; Physical Intelligence et al., 2025). Code as Policies instead composes perception and control APIs, while Inner Monologue incorporates environmental feedback (Liang et al., 2022; Huang et al., 2022). CaP-X and Guava study tool, action, and observation design (Fu et al., 2026; Liu et al., 2026a); Show-Harness and obstacle-aware harnesses address embodiment and geometric constraints (Chen et al., 2026; Xu et al., 2026). RoboDawn couples discrete commands with feedback and demonstrations (Guo et al., 2026a). Thea and PhyAgentOS structure persistent state and outcome verification (Wang et al., 2026b; Liu et al., 2026b), while Harness VLA and RoboHarness use execution memory to guide primitives or route heterogeneous policies (Zhang et al., 2026e; Huang et al., 2026). These systems establish the importance of the interface around a model. Self-improving agents. Reflexion retains verbal feedback, and GEPA evolves prompts from execution traces (Shinn et al., 2023; Agrawal et al., 2025). ADAS searches code-defined agents; SICA and DGM modify agent implementations (Hu et al., 2024; Robeyns et al., 2025; Zhang et al., 2025). AutoHarness, Meta-Harness, and Self-Harness synthesize or revise executable harnesses from feedback (Lou et al., 2026; Lee et al., 2026b; Zhang et al., 2026a). Recursive Harness Self-Improvement evolves a prompt-level harness with a fixed optimizer (Lee et al., 2026a), whereas STOP and Hyperagents also modify the improvement procedure (Zelikman et al., 2023; Zhang et al., 2026b). RRSI regularizes harness edits, and Dream-RSI uses discovery histories to improve exploration (Xia et al., 2026; Zheng et al., 2026). Hyperagents’ robotics experiment evolves rewards for PPO controllers (Zhang et al., 2026b, App. C.3); PhysEvo instead uses the same frozen model for robot decisions and software revision. Experience-driven embodied agents. REFLECT diagnoses failures from execution summaries (Liu et al., 2023); Voyager and RATs accumulate executable skills through interaction and autonomous play (Wang et al., 2023; Zhang et al., 2026c). EmbodiSkill distinguishes faulty skills from failures to follow valid guidance (Ju et al., 2026); ASPIRE retains manipulation and debugging knowledge from multimodal traces (Lu et al., 2026). RoboRSI organizes skills hierarchically (Noematrix Team, 2026), and Zetta evolves critics, recovery, and tools around a frozen action policy (Ding et al., 2026). SHAPER evolves skills and context-construction code with one frozen model serving as planner and optimizer, while fixing the action interface and executor (Wang et al., 2026a). ENPIRE automates real-world policy development (Xiao et al., 2026). RHO evolves multi-file robot repositories, including prompts, tools, and control code for LLM-in-the-loop execution (Elmaaroufi et al., 2026). HarnessPAI revises programs and repair skills across rollouts, keeping the selected high-level program fixed within each rollout (Wang et al., 2026c). PhysEvo focuses on the joint evolution of skills, robot-facing action and observation interfaces, and diagnostic resources: the same frozen model acts through revised interfaces and reuses self-built tools to diagnose later failures. 3 3PHYSEVO: PHYSICAL RECURSIVE SELF-IMPROVEMENT PhysEvo alternates between acting in the environment and revising the system used to act. A single frozen model performs both roles. Physical outcomes provide evidence; tested changes to the harness preserve what is learned from that evidence (figure 2). 1234 Astra Frozen weights Fixed contract World Evaluator Access Budget Astra Task agent Execution evidence TraceAction checkOutcome Astra Meta agent Edit Task Harness I/OSkillsMemory Meta Harness ReplayCodeTestMemory New episode Harness evolution v1v2 Retain & redeploy Fixed setupTask-time executionDevelopment-time improvementEvolution Packing task Figure 2: Physical RSI in PhysEvo. A frozen Astra acts through the task harness and revises both task and meta resources from execution feedback. Accepted revisions support later episodes and improvement rounds. 3.1ONE FROZEN MODEL, TWO EDITABLE HARNESSES LetM θ denote Astra with fixed parameters. At iterationr, the persistent system consists of a task harness H task r and a meta harness H meta r : H r = (H task r ,H meta r ).(1) The task harness supplies observations, action tools, execution feedback, and skills. Task-agnostic skills guide operations such as grasp verification and recovery; task-specific skills guide activities such as pouring or packing. The meta harness provides code inspection and editing, trajectory review, diagnostic experiments, and revision testing. The meta-agent may modify either harness, including its own tools. The two roles use separate contexts and tool access but the same model. Model weights, robot dynamics, and the task evaluator are not editable. Initialization provides a working observation–action loop with TCP targets and local IK, plus a general code- editing workspace. From this starting point, the system develops joint-level control, specialized observation tools, and reusable skills through execution and revision. 3.2TASK EXECUTION AND PHYSICAL FEEDBACK The task agent receives the instruction, selected skills, camera images, and robot proprioception. It selects an action or observation tool, then uses the returned result to make its next decision. Observation tools can retrieve evidence or calculate geometry without advancing the environment. Motion tools execute commands and return the resulting state and images. 4 For diagnosis, the harness links each requested action to its dispatched command and observed outcome. An unsuccessful grasp can thus be examined through approach, gripper closure, and subsequent object motion, rather than reduced to a final score. During execution, the same feedback supports closed-loop correction within the episode. 3.3REVISION, TESTING, AND RECURSIVE REUSE The meta-agent reviews execution alongside the current implementation, forms a failure hypothesis, and tests it through further inspection or diagnostic experiments. A revision may change action or observation tools, execution logic, skills, or diagnostic utilities. When a tool changes, its skill guidance is revised with it so that the agent inherits both a capability and instructions for using it. Candidate revisions are tested during development, not on the held-out layouts used for final scoring. The meta-agent first checks that edited code runs and tools return the intended outputs. For behavioral changes, it compares the candidate with the current harness on the same task and initial scene. A score gain, or progress beyond the failure without a score decrease, supports adoption; a score decrease leads to rejection. A diagnostic-tool revision is checked against the error or measurement it is intended to address. Accepted changes take effect between episodes. Changes to the task harness shape later execution; changes to the meta harness shape later diagnosis and revision. A review tool built in one round can therefore support a different correction in a later round. The improvement process thus inherits changes to its own machinery. A retained deployment version is then fixed for that task’s held-out evaluation (section 5.1). Section A gives the full procedure. 4THE EVOLVED EMBODIED FRAMEWORK Physical RSI reshapes what Astra can control, what evidence it can obtain, and what guidance persists. We describe the resulting architecture below; tasks retain their own deployment versions (section 5.1). 4.1JOINT-LEVEL CONTROL The defaultcontrol_armsinterface accepts twelve absolute arm-joint targets and two continuous gripper commands. It supports both arms and remains available while an object is held. The executor enforces native actuator limits and checks measured motion; Astra chooses the approach, arm configuration, and reconfiguration. TCP control asks the model for an end-effector goal and delegates posture selection to IK. Joint control instead makes posture part of the policy decision. This additional authority lets Astra adjust an obstructive configuration and continue manipulation, rather than repeatedly requesting a target from an unsuitable posture. Section C.1 compares the interfaces under fixed model, skill, initialization, and budget. 4.2ACTIVE AND RETROSPECTIVE OBSERVATION Astra can move a free arm to obtain a useful wrist-camera view while the other arm manipulates an object. When the relevant event is no longer visible,review_framesretrieves earlier images and aligned robot states from the current episode without advancing physics. Active viewing seeks new evidence; historical retrieval recovers evidence already observed. 5 Pixel projection and multi-view triangulation compute geometry for image features selected by Astra. Temporal triangulation uses views collected as a wrist camera moves. The model decides what to inspect and where to look; deterministic tools compute geometry and check validity using images and known camera and robot geometry, not hidden object coordinates. Temporal reconstruction requires a stationary point and a translational viewing baseline. 4.3REUSABLE SKILLS AND DIAGNOSTIC TOOLS Skills link tools to the physical conditions under which they are useful: when to verify a grasp, obtain another view, reorient an object, clear an obstacle, or check a release. The pouring skill, for example, maintains a free-arm side view through pouring and upright recovery. A packing skill requires wrist and gripper clearance above raised box flaps before turning. Skills retain these lessons while leaving action decisions to Astra’s closed-loop policy. The meta harness retains trajectory-review tools that align images, robot states, and policy decisions. The task agent uses runtime tools and selected skills; the meta-agent uses the broader editing and diagnostic workspace. Experience is therefore retained in two forms: guidance for acting and tools for understanding why an action failed. 5EXPERIMENTS We first evaluate retained deployment versions on held-out simulation layouts, then examine the revisions behind the resulting framework. On real hardware, we continue skill adaptation after deployment. 5.1EXPERIMENTAL SETUP Tasks and metrics. We evaluate all 42 base RoboDojo simulation tasks across generalization, precision, long-horizon execution, memory, and open-ended instruction following. Native score ranges from 0 to 100; success rate (SR) counts scores of 100. Each task averages one attempt on each of five held-out layouts. The headline comparison weights the five dimensions equally after averaging tasks within each dimension. We also report the equal-task average and two task-level comparisons covering 16 distinct tasks (tables 2 and 3). Development, sharing, and scoring. Simulation self-improvement precedes scoring. Development re- sources are shared across tasks; at the task-agent level, only tools and task-agnostic skills are shared, while task-specific skills remain task-local. Each task retains a deployment version that stays fixed across its five scored layouts. Scored layouts are excluded from revision and version selection. Before each scored episode, we reset session context, historical-frame indices, and writable memory. Online correction and same-episode history are allowed, but persistent tools and skills do not change. Section B details these boundaries. Comparison methods. References include RoboDojo robot and language-model policies (RoboDojo Team, 2026; Zhang et al., 2026d), GPT-as-Policy’s direct Astra and Astra–π 0.5 hybrid (Su et al., 2026), and RoboDawn’s one-shot Astra agent with closed-loop feedback and in-context demonstrations (Guo et al., 2026a). Bold and underlining indicate the best and second-best values within each comparison panel. 6 5.2PERFORMANCE ACROSS ROBODOJO Under five-dimension weighting, PhysEvo achieves 68.14/100 score and 62.00% SR, exceeding RoboDawn one-shot’s 47.17% SR by 14.83 points (table 1). This is the highest reported average among the compared methods, including an Astra agent already equipped with feedback and demonstrations. Equal weighting of all 42 tasks instead gives 65.00 score and 58.57% SR. Relative to RoboDawn, PhysEvo improves SR by 46.67 percentage points in memory, 17.50 in open-ended tasks, and 15.00 in precision. These gains combine stronger memory and instruction-dependent performance with improved precision (table 1). Table 1: RoboDojo performance across five capability dimensions. The average weights dimensions equally. MethodGeneral.PrecisionLong-horizonMemoryOpenAverage Score SR (%) Score SR (%) Score SR (%) Score SR (%) Score SR (%) Score SR (%) Robot policies Simate-beta35.0927.9534.3526.9257.8443.4233.3333.009.128.5033.9527.96 Liber-0 Preview24.9918.6138.2833.1745.9833.1737.7737.336.685.3330.7425.52 Liber-0 Lite25.3619.1136.6031.0845.5032.9235.6835.443.062.5829.2424.23 DM0.515.7810.9524.8216.7533.7019.5047.7447.442.432.0824.9019.34 GalaxeaVLA G0.518.4612.8328.2520.4244.1232.258.617.331.731.5820.2314.88 Xiaomi-Robotics-123.5517.0026.6918.8338.3923.677.816.563.943.5820.0713.93 OpenWAM-α20.7114.8418.459.2534.9325.3310.419.111.411.0817.1811.92 π 0.5 13.388.1712.405.5023.5414.675.894.671.981.6711.446.93 Language-model policies Official Astra33.3630.5012.654.0021.458.2543.0438.6734.3631.0028.9722.48 GPT-5.50.200.000.420.000.510.001.671.672.872.751.130.88 DeepSeek-Flash2.421.671.750.002.120.002.171.676.506.252.991.92 RoboDawn (1-shot)55.0850.0035.8825.0059.8845.0054.3353.3368.0062.5054.6347.17 PhysEvo (ours)51.0845.0050.3840.0056.3845.00100.00100.0082.8880.0068.1462.00 RoboDawn uses GPT-6 Astra with one in-context demonstration; its Score and SR follow the public evaluation records. 5.3COMPARISON WITH DIRECT AND HYBRID ASTRA POLICIES On the ten-task GPT-as-Policy set, PhysEvo reaches 68.00% SR, versus 26.00% for direct Astra and 48.00% for the hybrid (table 2). The three systems organize Astra’s capabilities differently: direct action, delegation to a trained VLA, or action through an experience-revised harness. PhysEvo improves SR over Direct on eight tasks and ties on two. Gains span language-conditioned classification, sorting imitation, and number arrangement, as well as packing and bottle disposal. An experience-revised harness thus offers a route to higher aggregate performance without a separately trained action policy. 7 Table 2: Comparison on the ten-task GPT-as-Policy evaluation set. Hybrid combines Astra withπ 0.5 ; Direct uses Astra alone. TaskG0.5XiaomiO-WAMHybridDirectPhysEvo ScoreSR ScoreSR ScoreSR ScoreSR ScoreSR ScoreSR Organize table46.3311.3357.6714.6762.5013.3360.000.0030.000.0060.000.00 Classify by language1.070.002.000.671.330.0038.0020.0060.0040.00100.00 100.00 Imitate sorting1.670.002.500.672.900.6753.0040.000.000.00 100.00 100.00 Largest number4.110.538.563.734.360.5350.0040.00 57.0040.00100.00 100.00 Pack objects17.122.9318.692.4020.833.7350.0020.0050.0020.0070.0040.00 Classify objects10.334.0017.208.675.531.3371.0060.00100.00 100.00 100.00 100.00 Build tower82.9378.6752.6047.3352.5338.6764.0060.0012.000.0040.0020.00 Make kong90.0090.0041.3341.3332.0032.0040.0040.000.000.0060.0060.00 Fold clothes32.7528.0041.4938.9351.3149.07 100.00 100.0040.0040.0068.0060.00 Bottles into dustbin96.3094.0097.7096.6794.0391.33 100.00 100.0036.0020.00 100.00 100.00 Mean38.2630.9533.9725.5132.7323.0762.6048.0038.5026.0079.8068.00 Score is on a 0–100 scale; SR is in percent. The three VLA references have the highest ten-task average scores in the source report (Su et al., 2026). G0.5 = GalaxeaVLA (G0.5); Xiaomi = Xiaomi-Robotics-1; O-WAM = OpenWAM-α. 5.4MANIPULATION TASKS CHALLENGING DIRECT ASTRA The RoboDojo Astra report identifies eight tasks with VLA SR≥ 20%and direct-Astra SR< 5%(Zhang et al., 2026d). This externally defined subset tests whether improvement reaches manipulation tasks that direct Astra rarely completes. PhysEvo achieves 55.00% SR and 62.88 score, compared with official Astra’s 1.25% and 5.96: a 53.75- point SR gain (table 3). Its mean SR also exceeds Simate-beta’s 50.83%, Liber-0 Preview’s 46.44%, and RoboDawn’s 45.00%. On the two pouring tasks, PhysEvo improves SR over RoboDawn from 0% to 80% for pouring balls and from 80% to 100% for pouring liquid. These gains align with the observation and pouring cases below. 8 Table 3: Eight manipulation tasks challenging direct Astra. PhysEvo results average five layouts per task. The task set is defined by the RoboDojo report, with VLA SR≥ 20% and Astra SR < 5%. TaskOfficial AstraG0.5Simate-betaLiber-0 PreviewRoboDawnPhysEvo ScoreSR ScoreSR ScoreSR ScoreSR ScoreSR ScoreSR Make kong0.000.0090.0090.0076.0076.0018.0018.0060.0060.0060.0060.00 Build tower16.402.0082.9378.6787.4083.3384.3381.0054.0040.0040.0020.00 Insert tubes6.000.0058.5342.6741.2022.0082.5373.0088.0080.0088.0080.00 Tic-tac-toe10.700.0065.2340.0086.2760.6795.67 89.00100.00 100.00 100.00 100.00 Pour balls into vase4.004.0028.0028.0045.3345.3330.6731.000.000.0080.0080.00 Pour liquid into cup4.004.0035.3435.3455.3355.3334.6734.5080.0080.00100.00 100.00 Laptop / headphones1.600.0028.2711.3453.8734.0038.1420.0012.000.0016.000.00 Fill pen holder5.000.0041.2718.6750.3730.0049.7725.0026.000.0019.000.00 Mean5.961.2553.7043.0961.9750.8354.2246.4452.5045.0062.8855.00 SR is in percent. G0.5 = GalaxeaVLA (G0.5); RoboDawn uses GPT-6 Astra, 1-shot. For Simate-beta and Liber-0 Preview, Std/Rand Score and SR are averaged within task before the eight-task mean. Further references appear in section D. 5.5WHAT CHANGES THROUGH PHYSICAL RSI? Three cases trace execution feedback to concrete changes in control, observation, and diagnosis. Making posture a policy decision. Figure 3 shows two independent executions: an obstructive fold under TCP control with local IK, and recovery from a rejected TCP motion through joint reconfiguration. End-effector goals leave posture to IK; joint control makes that missing choice expressible. In a focused two-layout comparison with the model, skill, initialization, and budget fixed, mean scores are 40 for TCP, 100 for Joint, and 55 for Both (six episodes; section C.1). Joint outperforming Both in this comparison also shows that a larger tool menu alone is not sufficient. TCP + local IK: unwanted arm foldingTCP + Joint: recovery through reconfiguration Figure 3: Joint reconfiguration enables recovery from an obstructive arm posture. Maintaining evidence during action. A pouring failure occurs while the bottle blocks the holding arm’s wrist view. Revising the pouring skill, with the other loaded skills fixed, directs the free arm to maintain a side view through pouring and recovery. The task agent then checks the bottle mouth, cup rim, liquid landing point, and clearance, corrects its motion, and completes the task (figure 4). The revised skill keeps existing sensors useful as manipulation alters visibility. 9 pouring failureMeta-AgentTask-Agent: revised Skill in use SKILL UPDATE Keep a side view with the free arm during pouring, draining and upright recovery. Check each motion. Pause and correct. Figure 4: Skill revision maintains a useful view throughout pouring and recovery. Inheriting a means of diagnosis. In figure 5, the meta-agent invokes a review tool built in an earlier round. Aligned images, robot states, and policy decisions help trace box displacement to wrist or gripper contact with raised flaps. The skill is then revised to require clearance above the flaps before turning. In the revised execution, the task agent follows this guidance and avoids pushing the box away. The final symptom, a box out of reach, thus leads to a specific intervention on the preceding contact. Crucially, a diagnostic resource created by one improvement round helps produce a correction in another: the system inherits a way to improve, not only a way to act. Recorded packing failureMeta-Agent: recursive reuseRevised Task-Agent Self-built earlier Review tool Reuse Review output Box too far Out of reach Wrist / gripper contact with raised box flaps pushes the box away Diagnose cause Revised Skill Clear wrist & gripper past the box flaps before turning Figure 5: An inherited review tool supports a later packing-skill revision. 5.6REAL-WORLD ADAPTATION THROUGH SKILL REVISION We deploy the simulation-evolved harness on an AgileX PiPER platform and continue the execution–revision loop. After each attempt, the meta-agent analyzes the trajectory and revises skills; the task agent uses those revisions in the next attempt. These cycles yield successful executions of block stacking, bowl stacking, pen placement, liquid pouring, and writing “PhysEvo” with a pen (figure 6). Hardware results. We report five trials per task, for 25 trials in total (table 4). PhysEvo achieves a mean score of 90.60/100 and 84.00% SR. Block stacking, bowl stacking, and pen placement each achieve 100% SR; pouring and writing each achieve 60% SR. Section B.3 specifies the task-level scoring rules. 10 Table 4: Real-world performance on AgileX PiPER. Each task has five trials. TaskScoreSR (%) Stack blocks100.00100.00 Stack bowls100.00100.00 Place pens100.00100.00 Pour water60.0060.00 Write PhysEvo93.0060.00 Overall90.6084.00 Two revisions illustrate how physical discrepancies change the inherited skills. Compensating for liquid motion. The liquid stream’s forward momentum causes it to overshoot the cup. The meta-agent revises the pouring skill to shift the bottle opening backward relative to the cup, compensating for the observed displacement. The hardware revision changes the action to account for the observed flow. Changing when geometry is used. Our physical arm has lower positioning accuracy than its simulated counterpart, and localization from the initial distant view is often unreliable. The revised skill reverses the geometry-first sequence: visual feedback guides a coarse approach, then geometry provides close-range localization for fine positioning. By changing the order of perception and action, the revised skill uses geometry under more reliable viewing conditions. These cases demonstrate continued post-deployment adaptation. Additional simulation executions appear in section E. 6DISCUSSION AND CONCLUSION PhysEvo uses physical consequences to revise the tools, skills, and diagnostic resources of a frozen Astra. Its retained deployment versions achieve 62.00% success under five-dimension weighting across 42 RoboDojo tasks. The cases identify three ways experience changes the system: exposing posture control, maintaining useful observations, and inheriting tools for later diagnosis. On AgileX PiPER, the same loop adapts skills to liquid motion and localization discrepancies, with a mean score of 90.60/100 and 84.00% success across five tasks. 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Corresponding: Yue Chen, Tianxing Chen. Yixian Zhang, Huanming Zhang, Feng Gao, Xiao Li, Zhihao Liu, Yi Nie, Chunyang Zhu, Jiaxing Qiu, Yuchen Yan, Jiyuan Liu, Wenhao Tang, Jiaji Rao, Zhengru Fang, Changxu Wei, Yu Wang, Wenbo Ding, and Chao Yu. Harness VLA: Steering frozen VLAs into reliable manipulation primitives via memory-guided agents. arXiv preprint arXiv:2607.08448, 2026e. URL https://arxiv.org/abs/2607.08448. Tong Zheng, Xidong Wu, Zheng Zhang, Zhankui He, Chaoyi Zhang, Benjamin Coleman, Ruoqiao Wei, Di Bai, Haolin Liu, Rui Liu, Xue Wang, Yue Zhuan, Wang-Cheng Kang, Renkai Xiang, Heng Huang, Xinwu Cheng, and Yunsong Guo. Dream-RSI: Recursive self-improvement through evolving worlds. arXiv preprint arXiv:2609.14858, 2026. URL https://arxiv.org/abs/2609.14858. 15 APHYSICAL RSI PROCEDURE At iterationr, the persistent system isH r = (H task r ,H meta r ). Both roles use the same frozen modelM θ . The task agent selects tools from a context assembled by the task harness: a t ∼ M θ (·| P H task r (ℓ,o ≤t ,m t )),(o t+1 ,f t ) = RunTool H task r (a t ).(2) Hereℓis the instruction,o ≤t contains observations,m t is episode memory, andf t is execution feedback. The indextcounts tool interactions. A motion call can execute several native steps; a review or geometry call need not advance the environment. Let E r contain feedback available to the meta-agent. One improvement round is summarized by e H r ← Revise M θ ,H meta r (H r ,E r ),(3) y r ← Test( e H r ,H r ),(4) H r+1 ← Select M θ ,H meta r (H r , e H r ,y r ).(5) e H r is a candidate revision andy r contains its test results. The selection step either adopts the candidate or preserves the current harness. Robot dynamics, actuator restrictions, model configuration, and evaluation rules are outside the editable system. Table 5: Physical RSI procedure in PhysEvo. Inputs: frozen Astra M θ , initial task and meta harnesses H 0 , and an interaction budget. 1 Execute a task using the current task harness. 2 Give the meta-agent the task outcome, relevant images and robot states, action feedback, and current code. 3 Let the meta-agent inspect the failure, retrieve evidence, and run diagnostic experiments. 4 Propose a candidate revision to tools, skills, execution logic, or meta resources. 5 Run the edited code and check its outputs. For task-behavior changes, compare the candidate with the current harness on the same task and initial scene. 6Adopt a candidate with a score gain, or progress beyond the failure without a score decrease. Reject a candidate that lowers the score. Test diagnostic changes against the error or measurement they address. 7 Load accepted tools and their associated skills together in subsequent episodes. Reuse accepted meta resources in subsequent improvement rounds. 8 Repeat within the interaction budget, then retain a deployment version for that task and evaluate held-out layouts without further updates. Initialization and persistence. The initial task harness supplies instructions, camera observations, proprio- ception, and a TCP/local-IK action interface. The initial meta harness supplies a code-editing and execution workspace. Retained tools and skills form a compatible system: an updated tool and the guidance for using its API are loaded together. Updates take effect between episodes rather than changing an active episode’s tools. Selected skills are loaded at episode start. The task agent uses its runtime tools; the meta-agent uses the code-editing workspace. Development resources are shared across tasks. At the task-agent level, tools and task-agnostic skills are shared, while task-specific skills remain task-local. Each task retains its own deployment version before scoring. 16 BEVALUATION DETAILS B.1METRICS AND AGGREGATION For task t, let s tℓ be the native score on layout ℓ∈1,..., 5. We compute M t = 1 5 5 X ℓ=1 s tℓ ,SR t (%) = 100 5 5 X ℓ=1 1[s tℓ = 100].(6) The ten-task GPT-as-Policy set and the eight-task manipulation subset overlap on tower building and making kong. Their union contains 16 distinct tasks and 80 attempts, giving 75.06 score and 65.00% SR. Each task is counted once in this union; the two subset averages are not averaged together. Development and held-out scoring. Self-improvement takes place during simulation development. For each task, a retained deployment version is fixed across its five scored layouts; these layouts are not used for harness revision or version selection. Development resources may carry experience across tasks, but task-agent sharing is restricted to tools and task-agnostic skills. Task-specific skills remain task-local. Episode state and information access. Before every scored episode, session context, historical-frame indices, and writable memory are reset. The task agent loads its retained tools and selected skills, then may accumulate and revisit observations from that episode and correct its actions online. This within-episode feedback does not change persistent tools, skills, or the deployment version. Scored instances do not supply privileged information to the meta-agent or contribute to revision or version selection. The hardware experiments use a separate adaptation protocol with skill revision after deployment; section 5.6 reports the real-world trial outcomes. B.2COMPARISON SOURCES AND AGGREGATION Five-dimension comparison. The robot and direct-language-model results in table 1 are taken from the RoboDojo leaderboard and Astra report (RoboDojo Team, 2026; Zhang et al., 2026d). The final average weights the five dimensions equally. RoboDawn’s five-dimension Score and SR are computed from its public evaluation records: GPT-6 Astra, 1-shot, with five runs per task (Guo et al., 2026b). The dimension-weighted means, 54.63 score and 47.17% SR, agree with its paper (Guo et al., 2026a). Direct and hybrid Astra policies. The ten-task set follows GPT-as-Policy (Su et al., 2026). PhysEvo is evaluated on five layouts per task, with one attempt per layout, for 50 attempts in total. The source reports 50 selected cases for the hybrid and 48 valid scores for Direct. We recompute Direct’s score by averaging task means, giving 38.50 rather than its pooled-case score of 37.81. The three VLA methods displayed in table 2 have the highest ten-task average scores among the source report’s VLA references. Manipulation-challenge subset. The eight tasks in table 3 correspond to Table F2 of the RoboDojo Astra report (Zhang et al., 2026d). Its selection criterion is VLA SR≥ 20%and Astra SR< 5%. PhysEvo is evaluated on five scenes per task on this subset, with one attempt per scene. The main table uses the same six methods as figure 1. Simate-beta and Liber-0 Preview follow the 23 September 2026 leaderboard update, recorded on 26 September. RoboDawn uses GPT-6 Astra, 1-shot, with five runs per task (Guo et al., 2026b). Both Score and SR are shown in the task rows. Section D retains the other robot-policy references. 17 B.3REAL-WORLD SCORES AND SUCCESS CRITERIA Each hardware task is evaluated over five trials. As in simulation, Score summarizes partial and full task completion on a 0–100 scale, while SR is the percentage of trials reaching the task’s maximum score. Averaging the five task-level metrics in table 4 gives a Score of 90.60/100 and an SR of 84.00%. The task-specific scores are defined below. Stack blocks. The goal is to stack three blocks. A score of 0 means that no block stacking is completed; 15 means that any two of the three blocks are stacked; 100 requires all three blocks to be stacked and the arm to return home. Stack bowls. The goal is to stack three bowls. A score of 0 means that no bowls are correctly stacked; 15 means that any two bowls are stacked upright; 100 requires all three bowls to be stacked upright, the bottom bowl to rest stably on the table, and the arm to return home. Place pens. The goal is to place both pens in the holder. Scores are 0 if neither pen is in the holder, 50 if either one is in the holder, and 100 if both are in the holder. Pour water. The goal is to pour liquid from the bottle into the cup. A score of 0 means that the liquid has not been successfully poured into the cup. A score of 100 requires the cup to contain the required liquid and the bottle to be upright. Write PhysEvo. The goal is to write “PhysEvo” on the table. The score is 0 when no letter is correct, and 15, 30, 45, 60, 75, or 90 when any one, two, three, four, five, or six letters are correct, respectively. All seven letters correct earns 100. The five scores are 100, 100, 100, 75, and 90, with a mean Score of 93/100 and an SR of 60%. CADDITIONAL FRAMEWORK ANALYSIS C.1CONTROL-INTERFACE COMPARISON A focused comparison on language-based object classification evaluates TCP, Joint, and Both interfaces on two scene layouts, for six episodes in total. The model, task skill, initialization for each layout, and budget are held fixed. Mean scores are 40 for TCP, 100 for Joint, and 55 for Both. Joint is retained as the default control interface. The control sequences in figure 3 illustrate two independent executions. The first develops an obstructive posture under TCP/local IK. In the second, Astra adjusts joints after a TCP rejection and then completes a motion to the same target pose. The mechanism is direct access to the arm configuration, which an end-effector target alone leaves to the IK solver. C.2OBSERVATION TOOLS Historical-frame retrieval returns images and aligned robot states from earlier in the same episode. It does not advance the environment. Active viewing can place the free wrist camera to the side of a manipulation. Geometric tools combine model-selected image features with camera calibration and robot geometry. 18 A synthetic-marker reconstruction test accepted 8 of 12 points, with a median error of 0.615 m and a 95th-percentile error of 1.437 m. Four points were rejected by the validity checks. A separate wrist-camera translation of approximately 57.525 m yielded a marker error of about 2.14 m. These component tests measure the calibration and reconstruction paths. Temporal triangulation additionally requires correspon- dence to the same stationary point across views. C.3SKILL-GUIDED OBSERVATION AND DIAGNOSIS The pouring example changes the skill guidance while keeping the other loaded skills fixed. The revised skill maintains a free-arm side view during pouring, draining, and upright recovery. The task agent uses the observation arm while the pouring arm makes corrections, and the revised episode reaches task success. The packing example reuses a trajectory-review tool that presents RGB, robot states, and policy decisions. The meta-agent inspects the box displacement and revises the packing skill to require clearance above the flaps before a turn. With the revised skill, the task agent avoids pushing the box away. The reused diagnostic tool thus supports a subsequent skill revision. 19 DROBOT AND AGENT AVERAGES ON THE MANIPULATION SUBSET The original 40 robot-policy means are reaggregated from the RoboDojo Astra report’s task matrix (Zhang et al., 2026d), weighting all eight tasks equally. The Simate-beta, Liber-0 Preview, and RoboDawn rows summarize the task-level Score and SR entries in table 3 (RoboDojo Team, 2026; Guo et al., 2026b). Each method is evaluated on the same task set and averaged with equal task weights. G0.5 is the strongest robot policy in the original 40-method set (43.09% SR); Simate-beta is the strongest displayed robot-policy entry in the extended comparison (50.83%). PhysEvo reaches 55.00%. Table 6: Eight-task means for the original references and the extended comparison. Bold and underlining mark the best and second-best values. MethodMean score SR (%) PhysEvo (ours)62.8855.00 Simate-beta61.9750.83 Liber-0 Preview54.2246.44 RoboDawn (1-shot)52.5045.00 GalaxeaVLA (G0.5)53.7043.09 Xiaomi-Robotics-144.7331.83 DM0.537.8026.42 OpenWAM-α34.1026.25 VLAct28.2622.67 Meituan-Robotics-026.7616.25 StarVLA-PI_v324.3818.50 InternVLA-A1.523.8416.00 Spatial Forcing23.7816.42 Hy-Embodied-0.5-VLA23.6115.17 StarVLA-OFT22.6815.08 X-VLA22.1215.25 Pi-0519.7312.00 StarVLA-GR00T15.959.00 StarVLA13.848.33 Xiaomi-Robotics-012.938.33 MolmoAct211.625.17 EventVLA11.425.92 GalaxeaVLA (G0)10.485.25 MethodMean score SR (%) GigaWorld-Policy-07.873.09 LingBot-VLA7.493.58 AHA-WAM7.292.50 X-WAM7.112.17 Pi-05.312.42 ABot-M04.611.67 SmolVLA (Single Task)3.221.17 GROOT-N1.73.221.25 InternVLA-A12.961.33 LDA-1B2.640.58 ACT (Single Task)2.581.75 GO-12.351.25 Fast-WAM1.580.00 RDT0.600.08 Dexora-1B0.480.00 H-RDT0.420.00 DM00.310.08 A10.110.00 TinyVLA0.090.00 OpenVLA-OFT0.090.00 Spirit v1.50.070.00 Official Astra5.961.25 20 EADDITIONAL SIMULATION EXECUTIONS Figures 7 to 12 present execution sequences for all 42 RoboDojo simulation tasks. Each strip retains the task name and instruction and shows six frames in temporal order from left to right. The gallery follows the S01–S42 case order. Organize Table:Place the mouse on the mouse pad, push the keyboard into the frame, put the figurine on the stand, place the alarm clock on the drawer, then open the drawer and put all remaining miscellaneous items inside. Classify Objects By Language:Put category 1 objects into the left basket, category 2 objects into the middle basket, and category 3 objects into the right basket, then reset the robot arm. Imitate Sorting Sequence:Observe the object placement order, remember it, then place the corresponding objects into the basket in the same order. Arrange Largest Number:Arrange the numbers from left to right to form the largest possible number, and place them on the pad. Pack Objects Into Box:Place all the objects into the box with their front sides facing left. Classify Objects:Sort the objects by category into the three baskets. Build Tower:Build a stable multi-layer tower with the wooden blocks and boards. Figure 7: Simulation executions, cases S01–S07 from top to bottom. Task names and instructions appear above each strip; frames progress from left to right. 21 Make Kong:Wait for the opponent to discard a tile, then declare a kong with the matching tiles. Fold Clothes:Fold the clothes neatly. Put Bottles Into Dustbin:Pick up the bottles and throw them into the dustbin, using handover when needed. Insert Tubes:Insert the three tubes into the rack one by one. Play Tic-Tac-Toe:Play tic-tac-toe as the first player and fill the board with the opponent. Pour Balls Into Vase:Pour all the balls from the cup into the vase. Pour Liquid Into Cup:Pour the liquid from the bottle into the cup. Figure 8: Simulation executions, cases S08–S14 from top to bottom. Task names and instructions appear above each strip; frames progress from left to right. 22 Store Laptop And Headphones:Hang the headphones on the headphone stand, close the laptop, then place it into the vertical laptop stand. Fill Pen Holder:Hold the pen holder with one hand, place all pens into it with the other hand, then put it back down. Stack Bowls:Stack the three bowls together. Push T:Push the T-shaped block to align it precisely with the gray T-shaped pad. Hang Mugs:Hang all the mugs on the mug rack. Sweep Blocks:Pick up the broom, hand it over to the right hand, then use the dustpan to sweep the blocks. Make Toast:Pick up two slices of bread, place them into the toaster, and press the lever down. Figure 9: Simulation executions, cases S15–S21 from top to bottom. Task names and instructions appear above each strip; frames progress from left to right. 23 Sort Nesting Dolls By Size:Arrange the five nesting dolls in a row from left to right, from smallest to largest. Stack Blocks:Stack the three blocks with different textures. Fasten Screws:Insert and tighten each screw into the nut of the same color. Plug In Charger:Plug the charger into the power strip. Play Xylophone:Pick up the mallet and strike all xylophone keys from left to right. Deposit Coin:Pick up the coin from the holder and insert it precisely into the coin bank. Insert Key:Pick up the key, hand it over to the other hand, insert it into the keyhole, then turn it. Figure 10: Simulation executions, cases S22–S28 from top to bottom. Task names and instructions appear above each strip; frames progress from left to right. 24 Fill Egg Holder:Place the four eggs from the basket into the egg holder, then close the lid. Play Stacking Toy:Place all stacking toy pieces onto the correct pegs. Cover Blocks:Cover the blocks from left to right, remember their colors, then uncover them in the order: red, green, and blue. Match And Pick From Conveyor:Remember the first object on the conveyor, then pick the matching object when it appears again. Swap T:Pick up the two T-shaped blocks, swap their positions, and place them back with the correct orientations. Press By Number:Press the two red buttons the required number of times according to the number cards, then press the blue button to confirm. Swap Blocks:Swap the two blocks using the empty mat, pressing the button after each move. Figure 11: Simulation executions, cases S29–S35 from top to bottom. Task names and instructions appear above each strip; frames progress from left to right. 25 Align Blocks:Use the set square to push the three blocks into a straight, aligned row, then reset the robot arm. Solve Equation:Complete the equation by selecting the correct missing number or operator and placing it on the pad, then reset the robot arm. Stack Blocks By Language:Stack the three blocks on top of each other in the order of color 1 , color 2 , and color 3 , then reset the robot arm. General Pickup:Pick up the target object by 10 cm. Pick From Conveyor By Image:Lift the basket more than 8 cm, identify the target object on the conveyor according to the image on the board, pick it up, and place it into the basket. Store Tools In Toolbox:Place each tool into its matching position in the toolbox, then reset the robot arm. Pour By Language:Pour the liquid from the first color 1 bottle into the first color 2 bowl, from the second color 3 bottle into the second color 4 bowl, and from the third color 5 bottle into the third color 6 bowl. Then reset the robot arm. Figure 12: Simulation executions, cases S36–S42 from top to bottom. Task names and instructions appear above each strip; frames progress from left to right. 26 FTASK-AGENT INPUT, OUTPUT, AND TOOL FORMATS The following blocks describe the task-agent interface in a joint-control configuration. Angle brackets and ellipses denote runtime data rather than literal argument values. F.1INPUT CONTEXT The system message supplies the control contract and selected skills. Each skill is Markdown text withname anddescriptionfields. A request also includes the preceding completed tool cycle, current public context, and RGB observations. Key context fields are: "instruction": "<current task instruction>", "step": <native-step index>, "state": "left_arm_joint_state": [<six measured joint angles>], "right_arm_joint_state": [<six measured joint angles>], "left_e_pose": [<x>, <y>, <z>, <qw>, <qx>, <qy>, <qz>], "right_e_pose": [<x>, <y>, <z>, <qw>, <qx>, <qy>, <qz>], "left_e_joint_state": [<gripper command echo>], "right_e_joint_state": [<gripper command echo>], "left_gripper_measured_joints": [<measured finger positions>], "right_gripper_measured_joints": [<measured finger positions>] , "memory": "<same-episode facts and unresolved questions>", "previous_result": <preceding tool feedback or null>, "budget": "remaining_native_steps": <integer>, "model_calls_remaining": <integer>, ... , ... Named RGB inputs include cam_head, cam_left_wrist, and cam_right_wrist, together with selected previous-segment or requested historical views. Other context fields provide home poses, joint semantics, and camera geometry. Joint angles are in radians; positions are in metres. Task and progress fields. instruction is the natural-language goal for the current task. step is the current native environment-step index: one motion call may advance several native steps, whereas a geometry or review call need not advance it. state groups the robot’s reported measurements at that step. Arm-state fields. left_arm_joint_state and right_arm_joint_state contain six measured joint angles for the corresponding arm. left_e_pose and right_e_pose give the reported end-effector position and orientation. In each seven-element pose, the first three entries arex,y,zin metres; the final four are the quaternion components in scalar-first order q w ,q x ,q y ,q z . Gripper-state fields. left_e_joint_state and right_e_joint_state echo gripper commands. left_gripper_measured_joints andright_gripper_measured_jointsinstead report measured finger positions. The distinction lets the agent compare what was commanded with what the gripper physically reached; neither a closing command nor a small opening by itself proves that an object is held. 27 Memory, feedback, and budgets. memory carries the task agent’s explicit same-episode notes, including established facts and unresolved questions. It is not a persistent skill document. previous_result carries the preceding tool’s feedback, or null when no preceding result is available. budget.remaining_native_steps is the remaining environment-step allowance, while budget.model_calls_remaining is the remaining model-call allowance. These are separate resources because a tool call can execute zero, one, or several native steps. Skills and image names. A skill’s name identifies the document and its description summarizes its purpose or applicability; its Markdown body supplies procedural guidance. The named cam_head, cam_left_wrist, and cam_right_wrist inputs identify the source camera for RGB observations. RGB observations are supplied as named image inputs alongside the JSON context. F.2MODEL OUTPUT AND FUNCTION CALLS The model returns exactly one enabled function call. Every tool requires assessment, plan, phase, and updated episodememory, followed by tool-specific arguments. Below,argumentsis displayed as a decoded object; the function-call transport serializes it as a JSON string. "type": "function_call", "call_id": "<call identifier>", "name": "control_arms", "arguments": "assessment": "<observed evidence and uncertainty>", "plan": "<next operation>", "phase": "fine", "memory": "<updated same-episode memory>", "left": null, "right": "control": "joint", "joints": [<q1>, <q2>, <q3>, <q4>, <q5>, <q6>], "gripper": null , "steps": <integer>, "settle": <integer> Call-envelope fields. type: function_call marks an executable tool request. call_id identifies that request and must match the identifier in its return message. name selects an enabled tool, here control_arms; arguments contains the fields validated against that tool’s schema. Common decision fields. assessment summarizes the currently observed evidence and remaining uncertainty. plan states the next intended operation. phase selects the fine-motion or transit regime and its permitted segment length. The output memory is the updated note carried into the next decision. 28 Arm-selection fields.leftandrightselect commands for the corresponding arms. A non-null arm object uses control: joint to select joint control, joints for its target vector, and gripper for its opening command. An arm or an individual target field set to null preserves that command. steps requests the native motion-segment length, while settle requests additional hold steps for settling. Units and allowed ranges are detailed below. F.3TOOL ARGUMENTS AND EXECUTION FEEDBACK Table 7 lists tool-specific arguments in addition to the four common fields above. Only enabled tools can be invoked; task-agent calls do not provide shell access or hidden simulator-state queries. Table 7: Task-agent tool arguments. Braced entries denote object fields, and square brackets denote lists. ToolTool-specific arguments and semantics control_arms left,right,steps,settle. Each non-null arm hascontrol,joints, and gripper fields, as shown above. review_frames frames: [step, camera]. Retrieves 1–6 same-episode RGB frames and their public poses without motion. project_pixels points: [pixel: [u,v], z]. Projects 1–32 head-view pixels onto model-specified height planes without motion. triangulate_point step, feature, views: [camera, pixel]. Matches one physical feature in 2–3 distinct current views; returns geometry estimates and diagnostics without motion. triangulate_temporal_point step, camera, feature, stationary_target, camera_hand_empty, stationarity_evidence, and samples: [step, pixel]. Uses 2–3 reviewed views from one wrist camera. Both Boolean fields must be true; sampling steps increase, end at the current step, and span at most 24 native steps. verify_completionNo additional arguments. Advances six hold steps and checks native termination. stop_episode reason. Stops the controller when blocked. Observation and geometry fields. Inreview_frames,framesis a list of retrieval requests:stepselects a same-episode observation by its native-step index andcameraselects its view. In geometric queries,pixel: [u,v] denotes horizontal and vertical image coordinates. The z in project_pixels specifies a model-chosen height plane in metres; it is a hypothesis, not a supplied object depth. In triangulate_point, step identifies the current observation, feature describes the same physical feature to match, and views pairs each camera with its pixel coordinates. Temporal geometry and stopping fields. For triangulate_temporal_point, camera selects one wrist camera, feature identifies the target feature, and samples pairs observation steps with its pixel locations. stationary_targetandcamera_hand_emptystate the required stationary-target and empty-camera-hand conditions; stationarity_evidence explains their observational basis. These flags must be supported by the observations. The reason argument of stop_episode explains why execution should stop. 29 Control parameters. Each non-null joints field contains six absolute targets in radians, not joint increments. gripper is a continuous opening command in [0, 1]. A null arm, joint vector, or gripper field holds its corresponding command. phase is fine or transit: motion segments use 1–6 or 1–20 native steps, respectively, with settle in0, 1, 2. Execution checks can stop a segment before the requested number of steps. Tool-result format. The result is linked to the model call through the same call_id. For a motion tool, feedback includes segment status, the executed step count, and tracking measurements. The following abbreviated block displays the serialized output payload as a decoded object: "type": "function_call_output", "call_id": "<same call identifier>", "output": "feedback": "status": "<segment status>", "reason": "<status detail>", "executed_steps": <integer>, "tracking": [<per-arm tracking measurements>], ... , "step": <current native-step index>, "ended": <Boolean> The next request supplies updated context and images together with this feedback. Observation and geometry tools instead return their requested evidence or diagnostic information without executing native actions. Return-envelope fields. type: function_call_output identifies a tool return. Its call_id links it to the preceding request, and output contains the tool’s returned payload. As with arguments, the block displays the decoded content rather than the transport’s JSON string. Execution-feedback fields. feedback.status describes the motion segment’s execution status, such as completion, interruption, or rejection. feedback.reason provides an associated explanation and may be null when no detail applies. feedback.executed_steps reports how many native steps actually ran, which can differ from the requested motion and settling steps. feedback.tracking contains per-arm measurements comparing commanded and observed execution. Updated step and termination. The return’s step is the updated native-step index after the tool call. ended indicates whether the episode has terminated, while task success is determined separately by the environment’s native evaluator. Updated state, images, and feedback allow the next call to respond to what actually happened. 30

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