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Learning to Act under Visual Interruptions with Vision-Language-Action Models
Mingle Jiang, Rui Xu, Yunke Wang, Chang Xu
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Abstract
Abstract:Vision-language-action (VLA) models have demonstrated strong capabilities in robotic manipulation, but they are typically developed and evaluated with all camera streams available throughout task execution. When a camera stops delivering frames during task execution, the policy must continue acting without access to subsequent observations from the missing view. Despite its practical importance, how such interruptions affect closed-loop manipulation remains insufficiently understood. To investigate this problem, we introduce MAIL-Bench, a benchmark that evaluates visual interruptions with VLA models. By interrupting different cameras at multiple stages of each policy's successful reference trajectory, MAIL-Bench measures how well policies retain their capabilities when visual inputs become unavailable. Building on this benchmark, we propose MINT, which first trains VLA policies to remain functional under missing visual inputs. At inference time, MINT selectively supplements missing observations using optical-flow extrapolation or an action-conditioned world model, and withdraws predicted views when they become unreliable. Experiments on $\pi_{0.5}$ and GR00T N1.5 show that MINT significantly improves task success under camera loss over the original models. Experiments on AgiBot G2 further demonstrate the real-robot deployment under camera loss. The benchmark is available at this https URL
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- Source: https://arxiv.org/abs/2609.35003v1
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Learning to Act under Visual Interruptions with Vision-Language-Action Models Mingle Jiang Affiliation: School of Computer Science, The University of Sydney Rui Xu Affiliation: School of Computer Science, The University of Sydney Yunke Wang Affiliation: School of Computer Science, The University of Sydney Chang Xu Affiliation: School of Computer Science, The University of Sydney Abstract Vision-language-action (VLA) models have demonstrated strong capabilities in robotic manipulation, but they are typically developed and evaluated with all camera streams available throughout task execution. When a camera stops delivering frames during task execution, the policy must continue acting without access to subsequent observations from the missing view. Despite its practical importance, how such interruptions affect closed-loop manipulation remains insufficiently understood. To investigate this problem, we introduce MAIL-Bench, a benchmark that evaluates visual interruptions with VLA models. By interrupting different cameras at multiple stages of each policy’s successful reference trajectory, MAIL-Bench measures how well policies retain their capabilities when visual inputs become unavailable. Building on this benchmark, we propose MINT, which first trains VLA policies to remain functional under missing visual inputs. At inference time, MINT selectively supplements missing observations using optical-flow extrapolation or an action-conditioned world model, and withdraws predicted views when they become unreliable. Experiments on π0.5 _0.5 and GR00T N1.5 show that MINT significantly improves task success under camera loss over the original models. Experiments on AgiBot G2 further demonstrate the real-robot deployment under camera loss. The benchmark is available at https://minglejiang.github.io/Mail-Bench/. 1 Introduction Vision-language-action (VLA) models (Brohan et al., 2023; Octo Model Team et al., 2024; Kim et al., 2025; Black et al., 2025b; Black et al., 2025a; NVIDIA et al., 2025; Xu et al., 2026a) use visual observations and language instructions to perform robotic manipulation. Standard evaluation benchmarks (Liu et al., 2023; Mees et al., 2022; James et al., 2020; Nasiriany et al., 2026) typically assume that all camera streams remain available throughout task execution. In practice, however, one or more camera streams may become unavailable while a robot is performing a task. Different cameras provide different views of the workspace: wrist cameras capture details near the robot’s gripper, while third-person cameras provide a broader view of the scene. When a camera becomes unavailable, the remaining views may not provide the information previously available from that camera. Unlike changes in lighting or camera pose (Pumacay et al., 2024; Fei et al., 2026; Wang et al., 2025), these visual interruptions remove a source of information altogether. This motivates a research question: How well can VLA models perform manipulation when some or all of their camera inputs are unavailable? Figure 1: MAIL-Bench. (a) Four availability conditions on RoboCasa365; an interrupted camera delivers no frame and its availability flag is 0. (b) Scenes solved out of 900 with every camera present (healthy) and after one visual role is lost at 30, 45 or 60% of each policy’s own completion time: camera loss removes a large share of both base policies’ successes, and MINT recovers part of that loss on every role. Existing robustness benchmarks (Pumacay et al., 2024; Chen et al., 2026; Fei et al., 2026; Wang et al., 2025; Hendrycks and Dietterich, 2019) primarily examine changes in visual appearance, camera pose, or image quality while keeping camera streams available. Missing camera inputs pose a different problem: a policy no longer receives observations from one or more of its original camera views. The impact may vary across cameras and tasks, depending on what information remains available from the other views. Yet existing robustness evaluations provide limited insight into how VLA models perform under these conditions. To investigate this problem, we introduce MAIL-Bench, a paired, closed-loop benchmark for evaluating VLA models with missing camera inputs. Built on RoboCasa365 (Nasiriany et al., 2024; Nasiriany et al., 2026), it covers 18 manipulation tasks and 900 frozen scenes. Rather than removing cameras individually, MAIL-Bench evaluates the loss of distinct visual roles: the wrist view, both third-person views, and all vision. For each policy, we first identify the scenes it can complete with all cameras available and use its successful rollouts as references. We then remove the relevant camera inputs at 30%–60% of the policy’s own completion time and keep them unavailable for the remainder of the episode. Missing frames are delivered as absent rather than replaced by the benchmark with blank or frozen images. By comparing paired rollouts in the same scenes and measuring success conditional on intact-camera success, MAIL-Bench quantifies how much capability a policy retains when visual inputs become unavailable. Training VLA models with missing camera inputs (Skand et al., 2025; Vanjani et al., 2025) enables them to continue acting without the unavailable views, but it cannot recover the visual information those views would have provided. Predicting missing views with action-conditioned video models (Guo et al., 2026; Bardhan et al., 2026) offers a way to temporarily supplement the remaining observations, although prediction errors may accumulate as the robot and scene change. We therefore propose MINT, which combines training for missing camera inputs with selective visual prediction. During fine-tuning, MINT explicitly marks unavailable cameras and excludes their visual tokens, allowing the policy to act using the inputs that remain. At inference time, it uses optical-flow extrapolation for missing wrist views or all-vision loss, and an action-conditioned world model (Bardhan et al., 2026) for missing third-person views. Predictions are retained only while checks based on available observations and commanded actions are satisfied. Otherwise, they are withdrawn, and the policy continues with the corresponding inputs marked as missing. Experiments on π0.5 _0.5 (Black et al., 2025a) and GR00T N1.5 (NVIDIA et al., 2025) reveal that the impact of missing camera inputs depends on which views are unavailable: losing a single wrist camera can be more disruptive than losing both third-person cameras. MINT improves task success under missing camera inputs on both models and, on GR00T N1.5, outperforms a matched clean fine-tuning control with the same training budget. We further deploy MINT on an AgiBot G2, where the same stack runs in real time and the robot completes manipulation tasks in closed loop with a camera interrupted partway through the task. 2 Related work Robustness Evaluation of Manipulation Policies. Generalist suites measure competence with intact observations (Liu et al., 2023; Nasiriany et al., 2024; Nasiriany et al., 2026; Mees et al., 2022; James et al., 2020; Li et al., 2023; Tao et al., 2025), and perturbation benchmarks (Pumacay et al., 2024; Chen et al., 2026; Fei et al., 2026; Wang et al., 2025; Li et al., 2025) change appearance, geometry, language, camera pose or the simulation-to-real gap, with every frame still delivered. Recent VLA methods have also improved robustness and generalization through task-aware visual selection and run-time intervention (Xu et al., 2026b; Feng et al., 2026). Common-corruption suites (Hendrycks and Dietterich, 2019) degrade image content in the same sense, and a comparative study weighs world action models against VLAs under such perturbations (Zhang et al., 2026). MAIL-Bench instead evaluates the complete loss of a visual stream during an ongoing rollout, rather than a change to the content of an available frame. A camera stops delivering frames altogether, at a moment defined by the evaluated policy’s own healthy trajectory rather than at a fixed step. The loss is applied per visual role rather than per device, the healthy and interrupted rollouts of a scene are paired by construction, and the outcome is scored conditional on healthy success (Table 1). Learning with Imperfect or Missing Modalities. Robust policy learning has been studied under imperfect supervision, including learning from suboptimal or noisy demonstrations (Wang et al., 2023b; Wu et al., 2019; Wang et al., 2021; Wang et al., 2024). Closer to our setting, prior work improves robustness to absent sensors through masked multimodal training, camera dropout, missing-state inference, retrieval, and availability-aware representations (Skand et al., 2025; Vanjani et al., 2025; Xu et al., 2025a; Vogt-Lowell et al., 2026; Ismkhan and Bouchahcia, 2026; Wang et al., 2023a; Kim and Hwang, 2026; Xu et al., 2025b; Gao et al., 2026). MINT’s fine-tuning stage follows the masked-training line without adding a component to the policy. Table 1: Comparison of visual-robustness evaluation settings. MAIL-Bench uniquely targets persistent visual-stream loss during closed-loop execution. Benchmark Visual failure Onset Post-loss control COLOSSEUM (Pumacay et al., 2024) Perturbation – – RoboTwin 2.0 (Chen et al., 2026) Perturbation – – VLATest (Wang et al., 2025) Perturbation – – LIBERO-Plus (Fei et al., 2026) Black frame Start Closed loop Missing-modality IL (Ismkhan and Bouchahcia, 2026) Camera dropout Start Open loop MAIL-Bench (ours) Camera loss Mid-episode Closed loop 3 MAIL-Bench: manipulation under visual interruption We introduce MAIL-Bench, a benchmark for evaluating how visual-stream interruptions affect manipulation policies on tasks they can otherwise complete. 3.1 Benchmark Construction Environment. We build MAIL-Bench on RoboCasa365 (Nasiriany et al., 2026), which provides diverse kitchen environments and everyday manipulation tasks. We select its 18 Atomic-Seen tasks to reduce the confounding effect of task generalization and focus on camera-loss robustness in previously learned tasks. With 50 fixed scenes per task, MAIL-Bench comprises 900 evaluation scenes. Availability conditions. To make camera-loss conditions comparable across policies with different camera configurations, we group cameras by their visual roles rather than by individual devices. The wrist camera forms the wrist group: it moves with the gripper and observes the manipulated object at close range. The onboard cameras form the third-person group: they are fixed to the robot base and provide broader views of the arm and workspace. Then we consider four availability conditions. Healthy keeps all cameras available and serves as the reference rollout. Wrist missing removes the wrist view, third-person missing removes all onboard views, and all vision missing removes all visual input, leaving only proprioception and the instruction. We use hard missing: once interrupted, a camera group provides no further frames and its availability flag is set to 0. Interruption timing. Camera loss may occur at different points during task execution. To evaluate this variation, we introduce interruptions at 30%, 45%, and 60% of each policy’s successful healthy completion time. Defining onset relative to the policy’s own execution accounts for differences in execution speed, rather than imposing the same absolute control step on every policy. Together, the three camera-loss conditions and three onset times define nine interruption settings, evaluated against a healthy reference. Figure 2: A successful healthy rollout defines the reference completion time ThealthyT_healthy. The same scene is then replayed with the selected visual role interrupted at 30%, 45%, or 60% of ThealthyT_healthy and kept unavailable thereafter, while unaffected cameras continue streaming. Shown is an AgiBot G2 drawer example. 3.2 Benchmark Evaluation Evaluation protocol. For each scene, we save the episode metadata and a settled simulator state immediately before policy execution. This state serves as the common starting point for the healthy rollout and all interruption rollouts. For each policy configuration, we first execute a healthy rollout with all cameras available. If the rollout succeeds, we record its completion step, ThealthyT_healthy; otherwise, the scene is excluded from interruption evaluation. Each interruption rollout restarts from the same saved initial state as the healthy rollout and uses the same policy random seed. It receives normal camera observations until the designated interruption control step, which, for onset fraction f∈0.30,0.45,0.60f∈\0.30,0.45,0.60\, is defined as Tf=min(Tmax−1,⌊f×Thealthy+0.5⌋),T_f= \! (T_ -1,\, f× T_healthy+0.5 ), (1) where TmaxT_ denotes the task horizon. Let ℛR denote the set of visual roles. The policy receives observations only when it is queried, every few control steps; for each role r∈ℛr , let IqrI_q^r denote its camera observation at the q-th query and mqr∈0,1m_q^r∈\0,1\ indicate whether it is available. When a camera becomes unavailable at TfT_f, the benchmark discards the remaining actions of the current chunk and queries the policy immediately; we denote this query by qfq_f. Before qfq_f, mqr=1m_q^r=1; for an interrupted role, from qfq_f onward mqr=0m_q^r=0 and the visual input is replaced by ∅ , indicating that no fresh frame is provided. After the interruption, subsequent frames from the interrupted camera group are withheld from both the policy and any recovery module. Any replacement view must therefore be generated causally from information available at runtime, such as pre-interruption observations, surviving camera streams, and executed actions, without access to future simulator frames or states. Each successful healthy rollout gives rise to nine fault rollouts, covering three camera-loss conditions and three interruption onsets. Deterministic execution settings and checks for pre-interruption divergence are used to reduce variation unrelated to the camera intervention. Evaluation metrics. We measure robustness only on scenes that the policy can already solve with all cameras available. For task j, let nj=50n_j=50 be the total number of scenes, NjHN^H_j the number solved under the healthy condition, and NjcN^c_j the number of those same scenes that remain successful under interruption condition c. We define Hj=NjHnj,Mc,j=NjcNjH,Sj=110(Hj+∑c=19Mc,j),SMAIL=118∑j=118Sj.H_j= N^H_jn_j, M_c,j= N^c_jN^H_j, S_j= 110 (H_j+ _c=1^9M_c,j ), S_MAIL= 118 _j=1^18S_j. (2) Here, HjH_j measures healthy task success, while Mc,jM_c,j measures how much of that capability is retained after interruption c. Conditioning Mc,jM_c,j on healthy success prevents robustness from being dominated by a policy’s baseline task competence. For each task, healthy performance and the nine interruption conditions are weighted equally, and the final SMAILS_MAIL score averages equally over all 18 tasks. We evaluate every interruption condition even if a policy does not normally rely on the corresponding camera, since remaining unaffected by its removal is itself evidence of robustness. If a policy has no healthy success on a task, that task receives a score of zero. 4 MINT: Acting under Visual Interruption Figure 3: MINT overview. MINT separates robustness to missing visual inputs from visual prediction. The policy is first trained to act under variable camera availability, then selectively receives predicted views at inference time when a camera is interrupted. Unreliable predictions are withdrawn, and the policy falls back to acting from the observations that remain. A robust VLA policy should remain functional when a camera becomes unavailable, without relying on the missing view being reconstructed. At the same time, when that view can be predicted reliably, the prediction may temporarily recover useful information that is no longer directly observed. MINT separates these two capabilities: learning with missing visual inputs teaches the policy to act from the observations that remain, while selective visual prediction supplements a missing view only when its prediction is considered reliable. At the q-th query, given visual observations Iqrr∈ℛ\I_q^r\_r , proprioceptive state sqs_q, and task instruction g, the policy predicts an action chunk of h actions a(q)∼πθ(⋅∣Iqrr∈ℛ,sq,g),a^(q) _θ\! (· \I_q^r\_r ,\,s_q,\,g ), (3) where h denotes the action horizon; only the first few actions are executed before the next query. Under visual interruption, one or more roles become unavailable during execution. MINT first adapts the policy to such changes in observation availability and then, at inference time, selectively supplies predicted observations for missing roles when reliable estimates are available. 4.1 Learning with Missing Visual Inputs We first train the policy to predict actions when only a subset of its usual visual observations is available. When mr=0m^r=0, the corresponding view is marked as missing and its visual tokens are excluded from attention, matching the hard-missing semantics of MAIL-Bench. Each training sample is a demonstration frame with observations Irr∈ℛ\I^r\_r , state s, instruction g, and the next h demonstrated actions a∗a . An availability pattern m∼m , sampled over the demonstration episode, determines which roles are missing at that frame, and I~r I^r denotes the input of role r under m. Using the backbone’s original action objective ℓ , we optimize ℒ(θ)=(Ir,s,g,a∗)∼,m∼[ℓ(πθ(⋅∣(I~r,mr)r∈ℛ,s,g),a∗)],L(θ)=E_(\I^r\,s,g,a ) ,\,m [ \! ( _θ\! (· \( I^r,m^r)\_r ,\,s,\,g ),a ) ], (4) where C is a distribution over temporal camera-availability patterns. It includes healthy, wrist-missing, third-person-missing, and all-vision-missing conditions, with both persistent and intermittent interruptions. The policy architecture, optimization schedule, and action objective are otherwise unchanged. The resulting policy can therefore continue acting from the observations that remain when no reliable estimate of a missing view is available. 4.2 Predicting Missing Visual Inputs Learning with missing visual inputs enables the policy to continue acting when a camera role is unavailable, but it cannot recover information that is no longer observed. At inference time, MINT therefore constructs a candidate prediction for a missing role and supplies it to the policy only while the prediction remains reliable. Let I^qr I_q^r denote a candidate prediction for visual role r at query q, and let cqr∈0,1c_q^r∈\0,1\ indicate whether that prediction is currently accepted. The observation presented to the policy is (xqr,m¯qr)=(Iqr,1),mqr=1,(I^qr,1),mqr=0∧cqr=1,(∅,0),mqr=0∧cqr=0,(x_q^r, m_q^r)= cases(I_q^r,1),&m_q^r=1,\\ ( I_q^r,1),&m_q^r=0\ \ c_q^r=1,\\ ( ,0),&m_q^r=0\ \ c_q^r=0, cases (5) where m¯qr m_q^r is the availability state seen by the policy. Thus, a missing role is either temporarily replaced by a predicted observation or left explicitly missing, in which case the policy falls back to the behavior learned in Section 4.1. Optical-flow prediction. The prediction mechanism depends on the visual role that becomes unavailable. For wrist interruption, and for all-vision interruption where no surviving camera is available to validate a generative rollout, we use short-horizon optical-flow extrapolation. From the last two available frames, we estimate Fr=Flow(Iqf−2r,Iqf−1r),I^qr=Warp(Iqf−1r,Fr,q−qf+1),q≥qf,F^r=Flow\! (I_q_f-2^r,I_q_f-1^r ), I_q^r=Warp\! (I_q_f-1^r,F^r,q-q_f+1 ), q≥ q_f, (6) where FlowFlow is Farnebäck optical flow (Farnebäck, 2003) and WarpWarp denotes short-horizon extrapolation from the last observed frame. This route is appropriate for wrist views, whose short-term visual dynamics are dominated by camera motion, and provides a conservative prediction when all visual streams are lost. World-model prediction. Third-person interruption requires modeling changes in both robot and object configuration that cannot be captured reliably by warping the last frame. We therefore use an action-conditioned world model. At each policy query q, the model predicts the missing third-person views together with an auxiliary wrist view: (I^qL,I^qR,I^qw)=ψ(ℳq−1,Iq−1w,a(q−1),g),( I_q^L, I_q^R, I_q^w)=G_ψ\! (M_q-1,I_q-1^w,a^(q-1),g ), (7) where ℳq−1M_q-1 is the model’s rollout memory, initialized at the interruption from the last available observations and updated with its own predictions, Iq−1wI_q-1^w is the surviving wrist observation, a(q−1)a^(q-1) is the committed action chunk, and g is the task instruction. We instantiate ψG_ψ with PersistWorld (Bardhan et al., 2026), adapted to the task domain with a low-rank fine-tune. Only I^qL I_q^L and I^qR I_q^R are supplied to the policy. The auxiliary wrist prediction I^qw I_q^w serves a different purpose: because the real wrist view remains observable, its agreement with I^qw I_q^w provides an online consistency signal for the otherwise unobserved third-person rollout. Section 4.3 describes how this signal, together with route-specific checks, determines cqrc_q^r. 4.3 Reliability-Gated Fallback Predicted observations are intended only as temporary substitutes for missing views. Because prediction errors can accumulate after interruption, MINT continuously monitors each prediction route using a set of route-specific reliability checks. A route remains active only while all of its checks are satisfied. Once rejected, its prediction is withdrawn for the remainder of the episode and the corresponding visual role falls back to the missing-input state learned in Section 4.1. For a missing visual role r, let zq,krk∈r\z_q,k^r\_k _r denote its reliability diagnostics at query q, where rK_r indexes the route-specific checks. We define route reliability as ρqr=∏k∈r[zq,kr∈r,k], _q^r= _k _r1 [z_q,k^r _r,k ], (8) where r,kA_r,k denotes the admissible range of the k-th reliability diagnostic (Table 4). Thus, ρqr=1 _q^r=1 only when all route-specific checks are satisfied. The acceptance state introduced in Eq. 5 is updated as cqr=cq−1r∧ρqr,cqfr=1.c_q^r=c_q-1^r _q^r, c_q_f^r=1. (9) This recursive update makes rejection irreversible within an episode: once cqr=0c_q^r=0, predicted observations for that role are no longer supplied to the policy. Optical-flow route. The optical-flow route uses two reliability diagnostics: consistency of the propagated view and accumulated robot motion. Both must remain within their respective admissible ranges in Eq. 8. If either check fails, ρqr=0 _q^r=0, causing the route to be rejected through Eq. 9. World-model route. The world-model route uses four reliability diagnostics: wrist consistency, visual change, model-distribution deviation, and rollout horizon. Wrist consistency compares an auxiliary predicted wrist observation I^qw I_q^w with the corresponding real wrist observation IqwI_q^w: dqw=D(I^qw,Iqw),d_q^w=D\! ( I_q^w,\,I_q^w ), (10) where D(⋅,⋅)D(·,·) measures normalized visual disagreement. The resulting dqwd_q^w, together with the other three diagnostics, forms the set of route-specific checks in Eq. 8. If any diagnostic falls outside its admissible range, ρqr=0 _q^r=0 and the route is rejected through Eq. 9. Once a prediction route is withdrawn, control continues through the policy’s learned missing-input capability rather than relying on continued visual reconstruction. 5 Experiments We evaluate MINT across two VLA backbones on MAIL-Bench, use matched controls to disentangle the contributions of learning with missing visual inputs and selective visual prediction, and further examine closed-loop deployment on a physical AgiBot-G2 robot. 5.1 Experimental Setup Models. We evaluate the RoboCasa365 Human300-trained checkpoints of π0.5 _0.5 (Black et al., 2025a) and GR00T N1.5 (NVIDIA et al., 2025), together with their respective MINT variants. The base models use their original policy implementations, and MINT is applied without modifying the underlying VLA architecture. All policies are evaluated on the frozen MAIL-Bench suite of 18 Atomic-Seen tasks and 900 scenes described in Section 3. We divide 18 tasks into 3 categories, which are 5 pick-and-place tasks, 6 small-appliance tasks, and 7 fixture and navigation tasks. We report the results of each category in Table 2. The complete task list and results are reported in Appendix C. Training. Both MINT policies are continued for 30k steps using the original action objective and optimization setup of their original checkpoints. Training uses a temporal availability curriculum over healthy, wrist-missing, third-person-missing and all-vision-missing observations with probabilities 0.60,0.17,0.17,0.06\0.60,0.17,0.17,0.06\. For fault episodes, persistent interruptions are sampled with probability 0.70.7, with onset uniformly drawn from [0.3,0.7][0.3,0.7] of the episode. The remaining cases contain short intermittent interruptions. Unavailable visual tokens are masked from policy computation rather than replaced by informative image content. Ablation Setup. For GR00T N1.5, we construct two matched variants to isolate the two main components of MINT. (1) Clean FT + prediction is fine-tuned for the same 30k steps using the same training data as MINT, but without missing-view examples during training. At inference time, it uses the same prediction mechanism as MINT. (2) Missing-view FT uses the same missing-view-trained checkpoint as MINT and the same serving cadence, but disables generated observations so that interrupted views remain missing. Thus, Clean FT + prediction versus MINT isolates the contribution of missing-view training under matched inference, while Missing-view FT versus MINT isolates the contribution of selective visual prediction with the policy weights held fixed. More implementation details are provided in Appendix B. Table 2: MAIL-Bench results. Healthy denotes success rate with all cameras available, while W/T/B report scores under wrist, third-person, and all-vision interruption at 30/45/60% of completion time. AVG is the MAIL-Bench score and Δ its improvement over the corresponding base checkpoint. Bold marks the best result within each backbone and task category. Method Tasks Healthy Wrist missing Third-person missing All vision missing AVG Δ W30 W45 W60 T30 T45 T60 B30 B45 B60 GR00T N1.5 (NVIDIA et al., 2025) base model pick-and-place 0.732 0.163 0.276 0.341 0.165 0.224 0.424 0.000 0.000 0.010 0.234 - small appliances 0.290 0.153 0.277 0.398 0.245 0.296 0.403 0.110 0.170 0.226 0.257 - fixtures & navigation 0.386 0.178 0.253 0.385 0.308 0.319 0.398 0.029 0.084 0.119 0.246 - +MINT pick-and-place 0.724 0.423 0.561 0.673 0.622 0.674 0.647 0.053 0.091 0.166 0.463 +0.229 small appliances 0.450 0.493 0.536 0.631 0.602 0.628 0.573 0.307 0.419 0.528 0.517 +0.260 fixtures & navigation 0.497 0.450 0.507 0.649 0.335 0.488 0.566 0.076 0.197 0.378 0.414 +0.168 π0.5 _0.5 (Black et al., 2025a) base model pick-and-place 0.636 0.059 0.098 0.200 0.239 0.245 0.375 0.000 0.014 0.089 0.196 - small appliances 0.257 0.270 0.264 0.342 0.291 0.343 0.413 0.065 0.095 0.156 0.250 - fixtures & navigation 0.360 0.151 0.266 0.355 0.453 0.469 0.602 0.043 0.129 0.254 0.308 - +MINT pick-and-place 0.688 0.027 0.111 0.342 0.574 0.735 0.713 0.025 0.090 0.196 0.350 +0.154 small appliances 0.333 0.291 0.426 0.350 0.513 0.372 0.480 0.218 0.337 0.370 0.369 +0.119 fixtures & navigation 0.414 0.131 0.261 0.528 0.428 0.498 0.546 0.093 0.128 0.278 0.331 +0.023 5.2 Benchmark Results Overall Results. As demonstrated in Table 2, MINT improves the performance of VLA base model even with healthy observations. On GR00T N1.5, for example, healthy success on the small-appliance tasks increases from 0.290 to 0.450. Under healthy observation setting, we only have model with availability finetuning and there is no visual generation process for the missing view. This suggests that learning under partial visual observations does not introduce a trade-off with normal execution and may reduce over-reliance on view-specific superficial cues. Once visual inputs are interrupted, we observe more clear improvement. Across 9 interruption conditions, MINT outperforms the GR00T N1.5 and π0.5 _0.5 base model in almost all cases. The gain spans wrist, third-person, and all-vision loss and persists across early and late interruption onsets, indicating that the benefit is not tied to a particular failure pattern. The consistent improvement across two VLA backbones (i.e., GR00T N1.5 and π0.5 _0.5) further suggests that MINT is not specific to a single policy architecture. Figure 4: Ablation on GR00T N1.5. Scores averaged over the three task categories at each interruption onset. “H” marks the healthy-input score of each variant. Ablation Study. MINT consists of two stages: missing-view training and selective visual prediction at inference and we ablate each on GR00T N1.5. Removing either stage degrades score, and Fig. 4 shows that the two stages cover different failure regimes. Missing-view training governs how far the policy falls when a view is lost early: without it, scores at the 30% onset drop to 0.29/0.32 under wrist/third-person loss, whereas late interruptions (W60) are largely recovered by prediction alone. Prediction, in turn, determines how much of the lost view can be recovered when another view remains: it is most effective under early third-person interruption (+0.105+0.105 at T30), where the global view can still be inferred from the wrist camera, and its gain shrinks toward the 60% onset as less of the task remains. Under all-vision loss the two curves coincide, since the only prediction available there is short-horizon flow extrapolation of the last frames. Figure 5: Episode on ‘Cube to Drawer’ task with the third-person camera interrupted at step 50. The policy completes grasping, transport, release and drawer closure. Top: real third-person stream, hidden from the policy after interruption. Middle: the third-person view stream and prediction by the world model. Bottom: the wrist stream, which stays available. Figure 6: Real robot experiment on the AgiBot G2. Success rate of GR00T N1.5 and MINT over 10 trials per task and condition. Numbers above the bars are successful trials. 5.3 Real Robot Experiment We next ask whether the same observation-interface design transfers to a physical robot without changing the underlying VLA architecture. We deploy MINT on an AgiBot G2 using an availability-aware GR00T N1.5 policy fine-tuned on 203 successful teleoperated episodes spanning four tabletop manipulation tasks. The G2 head camera provides the third-person view and the right wrist camera the wrist view. On all four tasks, we compare GR00T N1.5 with MINT under healthy execution and under third-person, wrist and all-camera interruption. The results are shown in Figure 6. We can observe that with MINT, GR00T N1.5 outperforms the base model in all missing-input settings and has no performance degradation under healthy setting. Figure 5 shows a drawer episode in which the third-person camera is interrupted mid-episode and the world-model route is activated. The healthy reference episode is shown in Appendix D (Figure 7(a)). After interruption the policy no longer receives real third-person camera frames. From the next query the world model supplies the missing view and the robot completes the task. 6 Conclusion In this paper, we propose a new benchmark MAIL-Bench, which provides a systematic evaluation of this problem and shows that interruption severity depends strongly on the visual role that is lost. MINT improves robustness by combining learning under missing visual inputs with selective visual prediction. Across two VLA backbones, it improves interruption robustness without modifying the policy architecture, and the same interface transfers to real-robot deployment. Future Work. MAIL-Bench currently covers one embodiment and 18 RoboCasa365 Atomic-Seen tasks, and broader validation across benchmarks and robot platforms remains future work. In addition, MINT’s world-model route introduces extra inference and memory overhead, motivating us to develop more efficient future prediction methods. References Bardhan et al. (2026) J. Bardhan, P. Drozdik, J. Sivic, and V. Petrik Persistent robot world models: stabilizing multi-step rollouts via reinforcement learning. In European Conference on Computer Vision (ECCV), Note: arXiv:2603.25685 Cited by: §1, §4.2. Black et al. (2025a) K. Black, N. Brown, J. Darpinian, K. Dhabalia, D. Driess, A. Esmail, M. R. Equi, C. Finn, N. Fusai, M. Y. Galliker, D. Ghosh, L. Groom, K. Hausman, B. Ichter, S. Jakubczak, T. Jones, L. Ke, D. LeBlanc, S. Levine, A. Li-Bell, M. Mothukuri, S. Nair, K. Pertsch, A. Z. Ren, L. X. Shi, L. Smith, J. T. Springenberg, K. Stachowicz, J. Tanner, Q. Vuong, H. Walke, A. Walling, H. Wang, L. Yu, and U. Zhilinsky π0.5 _0.5: A vision-language-action model with open-world generalization. In Proceedings of the 9th Conference on Robot Learning (CoRL), Proceedings of Machine Learning Research, Vol. 305, p. 17–40. Cited by: Appendix A, §1, §1, §5.1, Table 2. Black et al. (2025b) K. Black, N. Brown, D. Driess, A. Esmail, M. Equi, C. Finn, N. Fusai, L. Groom, K. Hausman, B. Ichter, et al. π0 _0: A vision-language-action flow model for general robot control. In Robotics: Science and Systems (RSS), External Links: Document Cited by: §1. Brohan et al. (2023) A. Brohan, N. Brown, J. Carbajal, Y. Chebotar, X. Chen, K. Choromanski, T. Ding, D. Driess, A. Dubey, C. Finn, et al. RT-2: vision-language-action models transfer web knowledge to robotic control. arXiv preprint arXiv:2307.15818. Cited by: §1. Chen et al. (2026) T. Chen, Z. Chen, B. Chen, Z. Cai, Y. Liu, Z. Li, Q. Liang, X. Lin, Y. Ge, Z. Gu, W. Deng, Y. Guo, T. Nian, X. Xie, Q. Chen, K. Su, T. Xu, G. Liu, M. Hu, H. Gao, K. Wang, Z. Liang, Y. Qin, X. Yang, P. Luo, and Y. Mu RoboTwin 2.0: a scalable data generator and benchmark with strong domain randomization for robust bimanual robotic manipulation. In International Conference on Machine Learning (ICML), Cited by: §1, §2, Table 1. Farnebäck (2003) G. Farnebäck Two-frame motion estimation based on polynomial expansion. In Image Analysis (SCIA), Lecture Notes in Computer Science, Vol. 2749, p. 363–370. External Links: Document Cited by: §4.2. Fei et al. (2026) S. Fei, S. Wang, J. Shi, Z. Dai, J. Cai, P. Qian, L. Ji, X. He, S. Zhang, Z. Fei, J. Fu, J. Gong, and X. Qiu LIBERO-Plus: a progressive robustness benchmark for visual-language-action models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), p. 38574–38583. Cited by: §1, §1, §2, Table 1. Feng et al. (2026) Y. Feng, Z. Zhao, Y. Ma, C. Xia, C. Du, Y. Wang, and C. Xu See what matters: differentiable grid sample pruning for generalizable vision-language-action model. In Forty-third International Conference on Machine Learning, Cited by: §2. Gao et al. (2026) H. Gao, Z. Li, X. Nie, D. Zhu, L. Zhao, Y. Wang, and C. Xu RA-sod: reliability-aware rgb-t salient object detection under modality degradation. arXiv preprint arXiv:2609.12622. Cited by: §2. Guo et al. (2026) Y. Guo, L. X. Shi, J. Chen, and C. Finn Ctrl-World: a controllable generative world model for robot manipulation. In International Conference on Learning Representations (ICLR), Note: arXiv:2510.10125 Cited by: §1. Hendrycks and Dietterich (2019) D. Hendrycks and T. Dietterich Benchmarking neural network robustness to common corruptions and perturbations. In International Conference on Learning Representations (ICLR), Cited by: §1, §2. Ismkhan and Bouchahcia (2026) H. Ismkhan and H. Bouchahcia Reinforcement learning-guided retrieval with soft fusion for robust multimodal imitation learning under missing modalities. arXiv preprint arXiv:2606.15514. Cited by: §2, Table 1. James et al. (2020) S. James, Z. Ma, D. R. Arrojo, and A. J. Davison RLBench: the robot learning benchmark & learning environment. IEEE Robotics and Automation Letters (RA-L) 5 (2), p. 3019–3026. External Links: Document Cited by: §1, §2. Kim and Hwang (2026) K. Kim and D. Hwang MUST: modality-specific representation-aware transformer for diffusion-enhanced survival prediction with missing modality. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), p. 30312–30321. Cited by: §2. Kim et al. (2025) M. J. Kim, K. Pertsch, S. Karamcheti, T. Xiao, A. Balakrishna, S. Nair, R. Rafailov, E. P. Foster, P. R. Sanketi, Q. Vuong, et al. OpenVLA: an open-source vision-language-action model. In Proceedings of the 8th Conference on Robot Learning (CoRL), Proceedings of Machine Learning Research, Vol. 270, p. 2679–2713. Cited by: §1. Li et al. (2023) C. Li, R. Zhang, J. Wong, C. Gokmen, S. Srivastava, R. Martín-Martín, C. Wang, G. Levine, M. Lingelbach, J. Sun, et al. BEHAVIOR-1K: a benchmark for embodied AI with 1,000 everyday activities and realistic simulation. In Proceedings of the 6th Conference on Robot Learning (CoRL), Proceedings of Machine Learning Research, Vol. 205, p. 80–93. Cited by: §2. Li et al. (2025) X. Li, K. Hsu, J. Gu, O. Mees, K. Pertsch, H. R. Walke, C. Fu, I. Lunawat, I. Sieh, S. Kirmani, S. Levine, J. Wu, C. Finn, H. Su, Q. Vuong, and T. Xiao Evaluating real-world robot manipulation policies in simulation. In Proceedings of the 8th Conference on Robot Learning (CoRL), Proceedings of Machine Learning Research, Vol. 270, p. 3705–3728. Cited by: §2. Liu et al. (2023) B. Liu, Y. Zhu, C. Gao, Y. Feng, Q. Liu, Y. Zhu, and P. Stone LIBERO: benchmarking knowledge transfer for lifelong robot learning. In Advances in Neural Information Processing Systems 36 (NeurIPS 2023) Datasets and Benchmarks Track, Vol. 36. Cited by: §1, §2. Mees et al. (2022) O. Mees, L. Hermann, E. Rosete-Beas, and W. Burgard CALVIN: a benchmark for language-conditioned policy learning for long-horizon robot manipulation tasks. IEEE Robotics and Automation Letters (RA-L) 7 (3), p. 7327–7334. External Links: Document Cited by: §1, §2. Nasiriany et al. (2024) S. Nasiriany, A. Maddukuri, L. Zhang, A. Parikh, A. Lo, A. Joshi, A. Mandlekar, and Y. Zhu RoboCasa: large-scale simulation of household tasks for generalist robots. In Robotics: Science and Systems (RSS), External Links: Document Cited by: §1, §2. Nasiriany et al. (2026) S. Nasiriany, S. Nasiriany, A. Maddukuri, and Y. Zhu RoboCasa365: a large-scale simulation framework for training and benchmarking generalist robots. In International Conference on Learning Representations (ICLR), Cited by: §1, §1, §2, §3.1. NVIDIA et al. (2025) NVIDIA, J. Bjorck, F. Castañeda, N. Cherniadev, X. Da, R. Ding, L. Fan, Y. Fang, D. Fox, F. Hu, et al. GR00T N1: an open foundation model for generalist humanoid robots. arXiv preprint arXiv:2503.14734. Cited by: Appendix A, §1, §1, §5.1, Table 2. NVIDIA (2025) NVIDIA NVIDIA Isaac GR00T: a foundation model for generalist robots. Note: https://github.com/NVIDIA/Isaac-GR00T Cited by: Appendix A. Octo Model Team et al. (2024) Octo Model Team, D. Ghosh, H. Walke, K. Pertsch, K. Black, O. Mees, S. Dasari, J. Hejna, T. Kreiman, C. Xu, et al. Octo: an open-source generalist robot policy. In Robotics: Science and Systems (RSS), External Links: Document Cited by: §1. Physical Intelligence (2025) Physical Intelligence Openpi: open-source models and packages for robotics. Note: https://github.com/Physical-Intelligence/openpi Cited by: Appendix A. Pumacay et al. (2024) W. Pumacay, I. Singh, J. Duan, R. Krishna, J. Thomason, and D. Fox THE COLOSSEUM: a benchmark for evaluating generalization for robotic manipulation. In Robotics: Science and Systems (RSS), External Links: Document Cited by: §1, §1, §2, Table 1. Skand et al. (2025) S. Skand, B. Pandit, C. Kim, L. Fuxin, and S. Lee Simple masked training strategies yield control policies that are robust to sensor failure. In Proceedings of the 8th Conference on Robot Learning (CoRL), Proceedings of Machine Learning Research, Vol. 270, p. 4463–4482. Cited by: §1, §2. Tao et al. (2025) S. Tao, F. Xiang, A. Shukla, Y. Qin, X. Hinrichsen, X. Yuan, C. Bao, X. Lin, Y. Liu, T. Chan, Y. Gao, X. Li, T. Mu, N. Xiao, A. Gurha, V. N. Rajesh, Y. W. Choi, Y. Chen, Z. Huang, R. Calandra, R. Chen, S. Luo, and H. Su Demonstrating GPU parallelized robot simulation and rendering for generalizable embodied AI with ManiSkill3. In Robotics: Science and Systems (RSS), External Links: Document Cited by: §2. Vanjani et al. (2025) P. Vanjani, P. Mattes, X. Jia, V. Dave, and R. Lioutikov DisDP: robust imitation learning via disentangled diffusion policies. Reinforcement Learning Journal 6, p. 1180–1199. Cited by: §1, §2. Vogt-Lowell et al. (2026) K. Vogt-Lowell, T. Tsiligkaridis, R. Lafuente-Mercado, S. Ghatti, S. Gao, M. Zitnik, and D. Rus When sensors fail: temporal sequence models for robust ppo under sensor drift. arXiv preprint arXiv:2603.04648. Note: ICLR 2026 CAO Workshop Cited by: §2. Wang et al. (2023a) H. Wang, Y. Chen, C. Ma, J. Avery, L. Hull, and G. Carneiro Multi-modal learning with missing modality via shared-specific feature modelling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), p. 15878–15887. External Links: Document Cited by: §2. Wang et al. (2024) Y. Wang, M. Dong, Y. Zhao, B. Du, and C. Xu Imitation learning from purified demonstrations. In Proceedings of the 41st International Conference on Machine Learning, ICML’24. Cited by: §2. Wang et al. (2023b) Y. Wang, B. Du, and C. Xu Unlabeled imperfect demonstrations in adversarial imitation learning. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37, p. 10262–10270. Cited by: §2. Wang et al. (2021) Y. Wang, C. Xu, B. Du, and H. Lee Learning to weight imperfect demonstrations. In International conference on machine learning, p. 10961–10970. Cited by: §2. Wang et al. (2025) Z. Wang, Z. Zhou, J. Song, Y. Huang, Z. Shu, and L. Ma VLATest: testing and evaluating vision-language-action models for robotic manipulation. Proceedings of the ACM on Software Engineering (FSE) 2 (FSE), p. 1615–1638. External Links: Document Cited by: §1, §1, §2, Table 1. Wu et al. (2019) Y. Wu, N. Charoenphakdee, H. Bao, V. Tangkaratt, and M. Sugiyama Imitation learning from imperfect demonstration. In International Conference on Machine Learning, p. 6818–6827. Cited by: §2. Xu et al. (2026a) S. Xu, Y. Wang, Z. Wang, D. Zhu, C. Xia, C. Du, D. Liu, T. Huang, and C. Xu StellaVLA: in-context structured demonstration for generalizable vision-language-action models. arXiv preprint arXiv:2608.11671. Cited by: §1. Xu et al. (2026b) S. Xu, Z. Wang, Y. Wang, C. Xia, T. Huang, and C. Xu Affordance field intervention: enabling vlas to escape memory traps in robotic manipulation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Cited by: §2. Xu et al. (2025a) X. Xu, D. Bauer, and S. Song RoboPanoptes: the all-seeing robot with whole-body dexterity. In Robotics: Science and Systems (RSS), External Links: Document Cited by: §2. Xu et al. (2025b) Y. Xu, F. Zhou, C. Zhao, Y. Wang, C. Yang, and H. Chen Distilled prompt learning for incomplete multimodal survival prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), p. 5102–5111. External Links: Document Cited by: §2. Zhang et al. (2026) Z. Zhang, Z. Li, B. Rahmati, R. H. Yang, Y. Ma, A. Rasouli, S. Pakdamansavoji, Y. Wu, L. Zhang, T. Cao, F. Wen, X. Wang, X. Quan, and Y. Zhang Do world action models generalize better than VLAs? A robustness study. arXiv preprint arXiv:2603.22078. Cited by: §2. Appendix A MAIL-Bench details Suite and horizons. The suite fixes the 18 Atomic-Seen tasks, 50 scenes per task and their horizons (Table 3) under RoboCasa365 1.0.1. Table 3: Task suite. The 18 RoboCasa365 Atomic-Seen tasks and their horizons in control steps (20 Hz). Task Horizon Task Horizon CloseBlenderLid 900 PickPlaceCounterToStove 600 CloseFridge 900 PickPlaceDrawerToCounter 750 CloseToasterOvenDoor 450 PickPlaceSinkToCounter 900 CoffeeSetupMug 600 PickPlaceToasterToCounter 600 NavigateKitchen 450 SlideDishwasherRack 450 OpenCabinet 1050 TurnOffStove 750 OpenDrawer 750 TurnOnElectricKettle 450 OpenStandMixerHead 450 TurnOnMicrowave 450 PickPlaceCounterToCabinet 750 TurnOnSinkFaucet 600 Base policies. We evaluate the RoboCasa365 Human300-trained checkpoints of π0.5 _0.5 (Black et al., 2025a) and GR00T N1.5 (NVIDIA et al., 2025), served through their native runtimes, openpi (Physical Intelligence, 2025) and Isaac-GR00T (NVIDIA, 2025). Under healthy observations, π0.5 _0.5 solves 362 of the 900 scenes (H=0.402H=0.402; 0.396 on the RoboCasa365 leaderboard) and GR00T N1.5 solves 405 (H=0.450H=0.450; 0.430 in the original paper and 0.507 on the leaderboard, re-evaluated under the 1.5× RoboCasa365 1.0.1 horizons that MAIL-Bench pins). Missing-view training does not reduce healthy success: on GR00T N1.5, Clean FT + prediction, Missing-view FT and MINT reach H=0.532H=0.532, 0.5400.540 and 0.5440.544 (Table 6). Auxiliary fault operators. The released kernel also implements blackout, freeze, stale delivery, burst dropout, finite-duration loss and recovery for diagnostic use; these are not part of the MAIL-Bench official score. Determinism. Policy sampling is seeded from the benchmark seed and the simulator is reset to the frozen scene state before every rollout, so paired rollouts differ only through the camera intervention, up to small GPU-renderer differences that MAIL-Bench records. Appendix B MINT Implementation Details B.1 Missing-view training We describe the GR00T N1.5 implementation; π0.5 _0.5 follows the same recipe within its native backbone. Training uses the curriculum C of Eq. 4 with the values in Table 4: a faulted episode loses a camera group either persistently from a random onset or in short intermittent bursts, and the two third-person cameras always share one availability state. Unavailable cameras are represented exactly as in the main method: their availability flag is 0, their image slot contains an inert zero placeholder, and their visual tokens are excluded from policy computation through the attention mask Attn(Q,K,V)=softmax(QK⊤d+logM)V,Mij=mr(j)ja visual token of group r(j),1otherwise,Attn(Q,K,V)=softmax\! ( QK d+ M )V, M_ij= casesm^\,r(j)&j\ a visual token of group r(j),\\ 1&otherwise, cases (11) with log0=−∞ 0=-∞, so an unavailable camera contributes zero attention weight while an available camera is processed as usual; changing the pixels of a masked camera leaves the actions unchanged. B.2 Serving and prediction routes Withdrawal. Withdrawal follows Eq. 9: once a route fails a check, the role returns to the missing input used in training for the rest of the episode. The optical-flow route is checked only after a minimum tenancy and has no upper limit on its duration; its propagated-view consistency is a flow confidence, the product of a photometric-consistency term, an accumulated-drift term and the fraction of pixels with a valid warp source. The world-model route checks every generated block from the first and is withdrawn at the first rejected block or at the generation cap. The two third-person cameras are admitted and withdrawn together. World-model route. We use PersistWorld because it is action-conditioned and sustains multi-step rollouts within the serving budget, and adapt it to the 18 tasks with a low-rank fine-tune on the demonstrations. On held-out demonstrations, adaptation raises reconstruction PSNR from 16.4 to 18.9 dB and lowers the fraction of out-of-domain conditioning states from 0.18 to 0.01. The rollout memory ℳM of Eq. 7 is initialized from the last available observations, and the auxiliary wrist prediction is used only for the consistency check. The same adaptation is used on the AgiBot G2 (Appendix D). B.3 Choice of Hyperparameters All hyperparameters were fixed on development scenes disjoint from the 900 benchmark scenes, before benchmark evaluation (Table 4). Table 4: Hyperparameters of MINT. Hyperparameter Value Optical-flow route Min. flow confidence (wrist / third-person) 0.05 / 0.08 Motion budget, wrist (end-effector + base) 0.15 m / 0.70 rad Motion budget, third-person (base) 0.30 m / 0.70 rad Minimum tenancy 64 steps World-model route Max. wrist consistency error dqwd_q^w 0.20 Visual change (max / min while moving) 0.35 / 0.002 Max. out-of-domain conditioning fraction 0.35 Generation cap 8 blocks (64 steps) Training and serving Curriculum C (healthy / wrist / third-person / all vision) 0.60 / 0.17 / 0.17 / 0.06 Persistent-interruption probability 0.7 Persistent onset (fraction of episode) [0.3,0.7]U[0.3,0.7] Fine-tuning steps 30k Query interval 8 steps Action horizon h (GR00T N1.5 / π0.5 _0.5) 16 / 50 Appendix C MAIL-Bench per-task and per-condition results All tables are generated from the scorer output of runs with complete healthy and fault coverage and no audit failure; Table 7 gives paired bootstrap intervals. The task categories of Table 2 are pick-and-place (the five PickPlace tasks), small appliances (CloseBlenderLid, CoffeeSetupMug, OpenStandMixerHead, SlideDishwasherRack, TurnOnElectricKettle, TurnOnMicrowave) and fixtures and navigation (CloseFridge, CloseToasterOvenDoor, NavigateKitchen, OpenCabinet, OpenDrawer, TurnOffStove, TurnOnSinkFaucet). In the per-task tables, NjHN^H_j is the number of healthy successes out of 50 scenes and “n/a” marks a task without healthy success, which contributes zero to SjS_j; values with small NjHN^H_j rest on few scenes. Table 5: Main results. Healthy success H, conditional scores McM_c under wrist (W), third-person (T) and all-vision (B) interruption at the 30/45/60% onsets, and SMAILS_MAIL (Eq. 2), averaged over the 18 tasks. Wrist missing Third-person missing All vision missing Policy H W30 W45 W60 T30 T45 T60 B30 B45 B60 SMAILS_MAIL π0.5 _0.5 0.402 0.165 0.219 0.307 0.340 0.365 0.476 0.038 0.086 0.176 0.257 π0.5 _0.5 + MINT 0.463 0.156 0.274 0.417 0.497 0.522 0.571 0.116 0.187 0.286 0.349 GR00T N1.5 0.450 0.165 0.267 0.377 0.247 0.285 0.407 0.048 0.089 0.124 0.246 GR00T N1.5 + MINT 0.544 0.457 0.532 0.650 0.504 0.586 0.591 0.147 0.242 0.369 0.462 Table 6: GR00T N1.5 ablation. All variants except the base are served at the same eight-step cadence. Clean FT + prediction is fine-tuned on the same data and steps as MINT without missing-view examples; Missing-view FT is the MINT checkpoint without generated views. Δ is SMAILS_MAIL minus the row above. Wrist missing Third-person missing All vision missing Arm H W30 W45 W60 T30 T45 T60 B30 B45 B60 SMAILS_MAIL Δ Base 0.450 0.165 0.267 0.377 0.247 0.285 0.407 0.048 0.089 0.124 0.246 Clean FT + prediction 0.532 0.294 0.369 0.580 0.316 0.419 0.455 0.079 0.191 0.279 0.351 +0.105 Missing-view FT 0.540 0.412 0.542 0.643 0.405 0.495 0.605 0.151 0.252 0.334 0.438 +0.086 MINT 0.544 0.457 0.532 0.650 0.504 0.586 0.591 0.147 0.242 0.369 0.462 +0.024 Table 7: Bootstrap 95% intervals. Scenes are resampled within each task (10,000 resamples), and differences between arms are paired on the same resampled scenes. Top: SMAILS_MAIL and paired differences. Bottom: per-condition paired differences between each backbone with MINT and its base. Arm SMAILS_MAIL 95% interval Paired difference ΔSMAIL S_MAIL 95% interval π0.5 _0.5 0.257 [0.221, 0.275] π0.5 _0.5 + MINT −- π0.5 _0.5 +0.091+0.091 [+0.063,+0.129][+0.063,+0.129] π0.5 _0.5 + MINT 0.349 [0.325, 0.365] GR00T N1.5 + MINT −- GR00T N1.5 +0.216+0.216 [+0.190,+0.240][+0.190,+0.240] GR00T N1.5 0.246 [0.227, 0.265] Clean FT + prediction −- GR00T N1.5 +0.105+0.105 [+0.079,+0.130][+0.079,+0.130] Clean FT + prediction 0.351 [0.332, 0.369] Missing-view FT −- Clean FT + prediction +0.086+0.086 [+0.061,+0.109][+0.061,+0.109] Missing-view FT 0.438 [0.417, 0.455] GR00T N1.5 + MINT −- Missing-view FT +0.024+0.024 [+0.008,+0.044][+0.008,+0.044] GR00T N1.5 + MINT 0.462 [0.443, 0.479] GR00T N1.5 + MINT −- Clean FT + prediction +0.111+0.111 [+0.087,+0.133][+0.087,+0.133] π0.5 _0.5 + MINT −- π0.5 _0.5 GR00T N1.5 + MINT −- GR00T N1.5 Condition ΔMc M_c 95% interval ΔMc M_c 95% interval W30 −0.010-0.010 [−0.068,+0.044][-0.068,+0.044] +0.291+0.291 [+0.235,+0.345][+0.235,+0.345] W45 +0.056+0.056 [−0.022,+0.145][-0.022,+0.145] +0.264+0.264 [+0.206,+0.321][+0.206,+0.321] W60 +0.110+0.110 [+0.018,+0.180][+0.018,+0.180] +0.273+0.273 [+0.208,+0.344][+0.208,+0.344] T30 +0.157+0.157 [+0.079,+0.237][+0.079,+0.237] +0.257+0.257 [+0.179,+0.331][+0.179,+0.331] T45 +0.158+0.158 [+0.095,+0.226][+0.095,+0.226] +0.301+0.301 [+0.223,+0.372][+0.223,+0.372] T60 +0.095+0.095 [+0.043,+0.175][+0.043,+0.175] +0.184+0.184 [+0.114,+0.249][+0.114,+0.249] B30 +0.078+0.078 [+0.042,+0.113][+0.042,+0.113] +0.099+0.099 [+0.067,+0.131][+0.067,+0.131] B45 +0.101+0.101 [+0.046,+0.159][+0.046,+0.159] +0.152+0.152 [+0.105,+0.196][+0.105,+0.196] B60 +0.110+0.110 [+0.065,+0.185][+0.065,+0.185] +0.245+0.245 [+0.198,+0.292][+0.198,+0.292] Table 8: Per-task, per-condition results for π0.5 _0.5. Task NjHN^H_j W30 W45 W60 T30 T45 T60 B30 B45 B60 SjS_j CloseBlenderLid 0 n/a n/a n/a n/a n/a n/a n/a n/a n/a 0.000 CloseFridge 23 0.70 0.57 0.74 0.09 0.17 0.39 0.09 0.26 0.57 0.403 CloseToasterOvenDoor 2 0.00 1.00 1.00 0.50 0.50 1.00 0.00 0.50 1.00 0.554 CoffeeSetupMug 5 0.00 0.00 0.00 0.20 0.20 0.20 0.00 0.00 0.00 0.070 NavigateKitchen 0 n/a n/a n/a n/a n/a n/a n/a n/a n/a 0.000 OpenCabinet 34 0.03 0.12 0.18 0.09 0.15 0.15 0.00 0.00 0.00 0.139 OpenDrawer 28 0.00 0.04 0.18 0.61 0.75 0.82 0.00 0.00 0.00 0.295 OpenStandMixerHead 27 0.81 0.85 0.96 0.56 0.63 0.67 0.11 0.19 0.22 0.554 PickPlaceCounterToCabinet 39 0.05 0.13 0.15 0.00 0.00 0.13 0.00 0.00 0.03 0.127 PickPlaceCounterToStove 38 0.16 0.18 0.26 0.45 0.55 0.87 0.00 0.03 0.26 0.352 PickPlaceDrawerToCounter 23 0.04 0.04 0.13 0.09 0.09 0.17 0.00 0.04 0.09 0.116 PickPlaceSinkToCounter 45 0.04 0.13 0.31 0.44 0.44 0.49 0.00 0.00 0.00 0.277 PickPlaceToasterToCounter 14 0.00 0.00 0.14 0.21 0.14 0.21 0.00 0.00 0.07 0.107 SlideDishwasherRack 32 0.50 0.50 0.62 0.53 0.69 0.84 0.12 0.16 0.56 0.517 TurnOffStove 9 0.33 0.11 0.22 0.89 0.78 0.89 0.11 0.11 0.11 0.374 TurnOnElectricKettle 13 0.31 0.23 0.46 0.46 0.54 0.77 0.15 0.23 0.15 0.357 TurnOnMicrowave 0 n/a n/a n/a n/a n/a n/a n/a n/a n/a 0.000 TurnOnSinkFaucet 30 0.00 0.03 0.17 1.00 0.93 0.97 0.10 0.03 0.10 0.393 Mean over 18 tasks 362 0.165 0.219 0.307 0.340 0.365 0.476 0.038 0.086 0.176 0.257 Table 9: Per-task, per-condition results for π0.5 _0.5 + MINT. Task NjHN^H_j W30 W45 W60 T30 T45 T60 B30 B45 B60 SjS_j CloseBlenderLid 2 0.00 0.50 0.00 0.50 0.00 0.00 0.00 0.00 0.00 0.104 CloseFridge 23 0.70 0.74 0.78 0.48 0.48 0.70 0.26 0.22 0.70 0.550 CloseToasterOvenDoor 9 0.11 0.22 0.56 0.11 0.22 0.33 0.22 0.22 0.33 0.251 CoffeeSetupMug 13 0.00 0.00 0.00 0.46 0.38 0.69 0.00 0.00 0.15 0.195 NavigateKitchen 1 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.102 OpenCabinet 36 0.06 0.22 0.31 0.11 0.36 0.25 0.00 0.03 0.03 0.208 OpenDrawer 31 0.00 0.16 0.45 0.58 0.74 0.81 0.03 0.13 0.26 0.378 OpenStandMixerHead 37 0.38 0.62 0.86 0.70 0.65 0.78 0.59 0.78 0.86 0.698 PickPlaceCounterToCabinet 39 0.05 0.15 0.62 0.67 0.87 0.79 0.03 0.21 0.46 0.463 PickPlaceCounterToStove 36 0.00 0.17 0.44 0.69 0.94 0.67 0.00 0.06 0.14 0.383 PickPlaceDrawerToCounter 26 0.04 0.08 0.23 0.27 0.38 0.35 0.08 0.12 0.31 0.237 PickPlaceSinkToCounter 44 0.05 0.05 0.27 0.50 0.77 0.80 0.02 0.00 0.00 0.333 PickPlaceToasterToCounter 27 0.00 0.11 0.15 0.74 0.70 0.96 0.00 0.07 0.07 0.335 SlideDishwasherRack 32 0.66 0.72 0.88 0.84 0.84 0.91 0.50 0.81 0.84 0.764 TurnOffStove 8 0.00 0.38 0.25 0.88 0.88 0.88 0.00 0.00 0.25 0.366 TurnOnElectricKettle 14 0.21 0.21 0.36 0.57 0.36 0.50 0.21 0.43 0.36 0.349 TurnOnMicrowave 2 0.50 0.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.104 TurnOnSinkFaucet 37 0.05 0.11 0.35 0.84 0.81 0.86 0.14 0.30 0.38 0.458 Mean over 18 tasks 417 0.156 0.274 0.417 0.497 0.522 0.571 0.116 0.187 0.286 0.349 Table 10: Per-task, per-condition results for GR00T N1.5. Task NjHN^H_j W30 W45 W60 T30 T45 T60 B30 B45 B60 SjS_j CloseBlenderLid 3 0.00 0.00 0.67 0.00 0.00 0.00 0.00 0.00 0.00 0.073 CloseFridge 35 0.57 0.71 0.74 0.17 0.31 0.37 0.20 0.29 0.46 0.453 CloseToasterOvenDoor 12 0.25 0.50 0.92 0.08 0.17 0.42 0.00 0.00 0.08 0.266 CoffeeSetupMug 5 0.00 0.00 0.00 0.00 0.00 0.20 0.00 0.00 0.00 0.030 NavigateKitchen 5 0.20 0.20 0.20 0.40 0.00 0.00 0.00 0.20 0.00 0.130 OpenCabinet 32 0.19 0.25 0.44 0.06 0.06 0.16 0.00 0.03 0.00 0.183 OpenDrawer 28 0.04 0.11 0.29 0.54 0.54 0.61 0.00 0.07 0.18 0.292 OpenStandMixerHead 26 0.31 0.58 0.77 0.27 0.42 0.69 0.19 0.23 0.31 0.429 PickPlaceCounterToCabinet 41 0.46 0.61 0.54 0.17 0.22 0.49 0.00 0.00 0.00 0.331 PickPlaceCounterToStove 41 0.02 0.20 0.41 0.12 0.22 0.46 0.00 0.00 0.02 0.228 PickPlaceDrawerToCounter 21 0.00 0.05 0.00 0.19 0.24 0.48 0.00 0.00 0.00 0.137 PickPlaceSinkToCounter 41 0.07 0.24 0.32 0.32 0.32 0.51 0.00 0.00 0.02 0.262 PickPlaceToasterToCounter 39 0.26 0.28 0.44 0.03 0.13 0.18 0.00 0.00 0.00 0.209 SlideDishwasherRack 24 0.38 0.62 0.58 0.79 0.71 0.79 0.33 0.38 0.54 0.560 TurnOffStove 5 0.00 0.00 0.00 0.40 0.60 0.40 0.00 0.00 0.00 0.150 TurnOnElectricKettle 22 0.09 0.32 0.23 0.41 0.50 0.59 0.14 0.27 0.36 0.335 TurnOnMicrowave 7 0.14 0.14 0.14 0.00 0.14 0.14 0.00 0.14 0.14 0.114 TurnOnSinkFaucet 18 0.00 0.00 0.11 0.50 0.56 0.83 0.00 0.00 0.11 0.247 Mean over 18 tasks 405 0.165 0.267 0.377 0.247 0.285 0.407 0.048 0.089 0.124 0.246 Table 11: Per-task, per-condition results for GR00T N1.5 + MINT. Task NjHN^H_j W30 W45 W60 T30 T45 T60 B30 B45 B60 SjS_j CloseBlenderLid 2 0.00 0.00 0.00 0.50 0.50 0.00 0.00 0.00 0.00 0.104 CloseFridge 30 0.83 0.70 0.80 0.30 0.53 0.63 0.23 0.20 0.37 0.520 CloseToasterOvenDoor 19 0.26 0.37 0.63 0.37 0.26 0.37 0.00 0.05 0.32 0.301 CoffeeSetupMug 11 0.18 0.18 0.64 0.36 0.36 0.64 0.09 0.00 0.09 0.277 NavigateKitchen 14 0.43 0.57 0.50 0.00 0.07 0.14 0.00 0.00 0.07 0.207 OpenCabinet 41 0.78 0.73 0.78 0.22 0.39 0.54 0.05 0.07 0.15 0.453 OpenDrawer 36 0.28 0.58 0.81 0.64 0.75 0.81 0.08 0.31 0.78 0.575 OpenStandMixerHead 34 0.88 0.97 0.94 0.62 0.59 0.62 0.65 0.82 1.00 0.777 PickPlaceCounterToCabinet 42 0.71 0.79 0.88 0.83 0.90 0.76 0.24 0.38 0.57 0.691 PickPlaceCounterToStove 40 0.25 0.47 0.55 0.75 0.62 0.47 0.00 0.00 0.00 0.392 PickPlaceDrawerToCounter 19 0.05 0.05 0.21 0.21 0.32 0.53 0.00 0.00 0.11 0.185 PickPlaceSinkToCounter 42 0.33 0.57 0.88 0.74 0.79 0.79 0.00 0.02 0.02 0.498 PickPlaceToasterToCounter 38 0.76 0.92 0.84 0.58 0.74 0.68 0.03 0.05 0.13 0.550 SlideDishwasherRack 39 0.82 0.85 0.82 0.72 0.74 0.82 0.49 0.82 0.90 0.775 TurnOffStove 10 0.40 0.30 0.40 0.40 0.70 0.60 0.00 0.50 0.30 0.380 TurnOnElectricKettle 34 0.47 0.62 0.59 0.88 0.97 0.76 0.35 0.47 0.65 0.644 TurnOnMicrowave 15 0.60 0.60 0.80 0.53 0.60 0.60 0.27 0.40 0.53 0.523 TurnOnSinkFaucet 24 0.17 0.29 0.62 0.42 0.71 0.88 0.17 0.25 0.67 0.465 Mean over 18 tasks 490 0.457 0.532 0.650 0.504 0.586 0.591 0.147 0.242 0.369 0.462 C.1 Compute cost All experiments run on NVIDIA GeForce RTX 4090 GPUs (24 GB). Evaluating one arm on MAIL-Bench takes 60–70 single-worker hours, and fine-tuning takes 12.9 GPU-hours for GR00T N1.5 (30k steps, one GPU) and 15.1 GPU-hours for π0.5 _0.5 (30k steps, two GPUs). A policy query costs about 5.0 TFLOPs for π0.5 _0.5 and 2.5 TFLOPs for GR00T N1.5. MINT does not increase the per-step wall-clock time of healthy execution; under interruption, the optical-flow route runs on the CPU, whereas the world model adds 9.2 GiB of GPU memory and about 100 TFLOPs per generation, with 7.35 (GR00T N1.5) and 7.72 (π0.5 _0.5) generations per third-person-missing cell on average. Appendix D Real-Robot Setup The AgiBot G2 has a head-mounted camera and one camera on each wrist, and the right arm manipulates. The head camera supplies the third-person group and the right wrist camera the wrist group. We collected 224 teleoperated episodes of four tabletop tasks and fine-tune an availability-aware GR00T N1.5 checkpoint on the 203 successful ones. Figure 7 shows a healthy episode of each task. (a) Drawer. (b) Cube into bowl. (c) Cup onto plate. (d) Stack cups. Figure 7: Healthy episodes of the four AgiBot G2 tasks. (a) drawer, (b) cube into bowl, (c) cup onto plate, (d) stack cups. Each panel shows the head (third-person) camera (top) and the right wrist camera (bottom) at five time points.