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ASTRIL-MPC: Autonomous Traversal Framework of Articulated Tracked Robots with Language-Guided Neural-Kinematic MPC
Zhenfeng Gan, Yanbo Chen, Lirong Che, Junbo Tan, Xueqian Wang
Intelligence
Status: succeeded | Model: Gemma-4-26B-A4B | Prompt: intel-v1 | Confidence: 94%
Last extracted: 9/20/2026, 2:57:47 AM
Summary
The paper introduces ASTRIL-MPC, a framework for autonomous traversal of articulated tracked robots (ATRs) in urban search and rescue scenarios. It combines a learned neural kinematics model, non-linear model predictive control (NMPC), and a large language model (LLM) for safety-checked weight retuning. The system aims to handle hybrid robot-terrain interactions and flipper-track coordination, claiming significant performance improvements over baselines. However, the paper was withdrawn by the author.
Entities (6)
Relation Signals (6)
Zhenfeng Gan → withdrew → ASTRIL-MPC
confidence 100% · This paper has been withdrawn by Zhenfeng Gan
ASTRIL-MPC → uses → Neural Kinematics Model
confidence 95% · A learned kinematics model predicts short-horizon task-state increments...
ASTRIL-MPC → uses → NMPC
confidence 95% · NMPC plans with multi-objective costs and strict feasibility constraints
ASTRIL-MPC → uses → Large Language Model
confidence 95% · a large language model (LLM) proposes bounded updates to selected weights and bounds
ASTRIL-MPC → outperforms → NMPC
confidence 90% · ASTRIL-MPC improves an aggregate traversal-quality score by up to 71% over a non-adaptive NMPC
ASTRIL-MPC → outperforms → PPO
confidence 90% · ASTRIL-MPC improves an aggregate traversal-quality score by ... 67% over a PPO baseline
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Abstract
Abstract:In urban search and rescue, articulated tracked robots (ATRs) must traverse structured but contact-rich environments such as stairwells and cluttered building interiors. Reliable autonomy remains challenging because robot-terrain interaction (RTI) is hybrid and discontinuous, and effective flipper-track coordination is difficult to model analytically. We present ASTRIL-MPC, a language-guided neural kinematics model predictive control (MPC) framework for autonomous traversal. A learned kinematics model predicts short-horizon task-state increments from a height sequence and recent trajectories; NMPC plans with multi-objective costs and strict feasibility constraints; and a large language model (LLM) proposes bounded updates to selected weights and bounds through a safety-checked interface with range clipping, rate limiting, and consistency checks. The compiled predictor enables a full control cycle within 100 ms. Across three traversal tasks and a multi-height generalization setting, ASTRIL-MPC improves an aggregate traversal-quality score by up to 71% over a non-adaptive NMPC and by 67% over a PPO baseline, while eliminating measurable collision impacts during descent. These results indicate that combining learned kinematics, optimization-based planning, and language-guided retuning yields data-efficient and robust autonomy for articulated tracked robots.
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- Source: https://arxiv.org/abs/2609.13083v2
- Canonical: https://arxiv.org/abs/2609.13083v2
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Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Robotics arXiv:2609.13083v2 (cs) This paper has been withdrawn by Zhenfeng Gan [Submitted on 11 Sep 2026 (v1), last revised 15 Sep 2026 (this version, v2)] Title:ASTRIL-MPC: Autonomous Traversal Framework of Articulated Tracked Robots with Language-Guided Neural-Kinematic MPC Authors:Zhenfeng Gan, Yanbo Chen, Lirong Che, Junbo Tan, Xueqian Wang View a PDF of the paper titled ASTRIL-MPC: Autonomous Traversal Framework of Articulated Tracked Robots with Language-Guided Neural-Kinematic MPC, by Zhenfeng Gan and 4 other authors No PDF available, click to view other formats Abstract:In urban search and rescue, articulated tracked robots (ATRs) must traverse structured but contact-rich environments such as stairwells and cluttered building interiors. Reliable autonomy remains challenging because robot-terrain interaction (RTI) is hybrid and discontinuous, and effective flipper-track coordination is difficult to model analytically. We present ASTRIL-MPC, a language-guided neural kinematics model predictive control (MPC) framework for autonomous traversal. A learned kinematics model predicts short-horizon task-state increments from a height sequence and recent trajectories; NMPC plans with multi-objective costs and strict feasibility constraints; and a large language model (LLM) proposes bounded updates to selected weights and bounds through a safety-checked interface with range clipping, rate limiting, and consistency checks. The compiled predictor enables a full control cycle within 100 ms. Across three traversal tasks and a multi-height generalization setting, ASTRIL-MPC improves an aggregate traversal-quality score by up to 71% over a non-adaptive NMPC and by 67% over a PPO baseline, while eliminating measurable collision impacts during descent. These results indicate that combining learned kinematics, optimization-based planning, and language-guided retuning yields data-efficient and robust autonomy for articulated tracked robots. Comments: wrong paper uploaded Subjects: Robotics (cs.RO); Artificial Intelligence (cs.AI) Cite as: arXiv:2609.13083 [cs.RO] (or arXiv:2609.13083v2 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2609.13083 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Zhenfeng Gan [view email] [v1] Fri, 11 Sep 2026 17:17:52 UTC (32,242 KB) [v2] Tue, 15 Sep 2026 12:54:05 UTC (1 KB) (withdrawn) Full-text links: Access Paper: View a PDF of the paper titled ASTRIL-MPC: Autonomous Traversal Framework of Articulated Tracked Robots with Language-Guided Neural-Kinematic MPC, by Zhenfeng Gan and 4 other authorsWithdrawn No license for this version due to withdrawn Current browse context: cs.RO < prev | next > new | recent | 2026-09 Change to browse by: cs cs.AI References & Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) mathjaxToggle(); We gratefully acknowledge support from our major funders, member institutions, , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from