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ASTRIL-MPC: Autonomous Traversal Framework of Articulated Tracked Robots with Language-Guided Neural-Kinematic MPC

Paper recorded by Signals 4 on 2026-09-11 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-11 on arXiv · recorded by Signals 4 on 2026-09-14

Category: cs.AI · 人工智能 · first seen 2026-09-14

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 neura

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#84 most recent of 300 cs.AI papers we have recorded · ↑ newer: CMA-OT: Hierarchical Expert Supervision for Dance-to-Music Generation · ↓ older: Embodied-BenchForge: A Closed-Loop Agentic Workflow for Embodied Bench
Cite this page: ASTRIL-MPC: Autonomous Traversal Framework of Articulated Tracked Robots with Language-Guided Neural-Kinematic MPC: the #84 most recent of 300 cs.AI papers we have recorded (as of 2026-09-11). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/astril-mpc-autonomous-traversal-framework-of-articulated-tracked-robots-with-lan.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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