cs.RO · 2026-09-11 · No. 112

Robotics, 2026-09-11.

3 new papers in cs.RO. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

3 entries
  1. 01

    Reflex-Informed Neuromuscular Reinforcement Learning for Muscle-Driven Locomotion

    Jian Zhou, Xingyu Zhang, Rui Ma, Yu Cao, Shane Xie, Zhi-qiang Zhang

    cs.RO · cs.GR · cs.LG

    Muscle-driven locomotion provides a physically grounded approach to generating realistic human movement. However, achieving both physiological plausibility and adaptability to changes in musculoskeletal capacity and external disturbances remains a fundamental challenge. To address this limitation, we propose a Reflex-Informed Neuromuscular Reinforcement Learning framework for muscle-driven locomotion. Within this framework, a fixed...

    arxiv.org/abs/2609.11733 · PDF

  2. 02

    ActSafeGuard: Differentiable and Training-Aligned Constraint Enforcement for Flow-Matching Policies

    Jianming Ma, Rongjun Jin, Xiaxi Si, Yang Zhang, Yiheng Li, Yue Gao

    cs.RO · cs.AI

    Vision-Language-Action (VLA) and World-Action Models (WAMs) have demonstrated strong capabilities in general-purpose robotic manipulation, yet their generated actions may violate hard physical constraints and therefore be unsafe or infeasible for deployment. Existing safety approaches either optimize statistical safety objectives without deterministic per-step guarantees or correct unsafe actions only during inference, creating a mismatch...

    arxiv.org/abs/2609.11697 · PDF

  3. 03

    2AM: Grounding Agent-Side Memory as Guidance for Steerable Action Models in Long-Horizon Manipulation

    Yutong Hu, Fengjiao Chen, Xuezhi Cao, Renaud Detry

    cs.RO · cs.AI

    Long-horizon robot manipulation requires memory, but not necessarily inside the action policy. To address such tasks, current agentic systems often combine VLAs with planners and geometric tools, sometimes using additional depth or calibrated geometry. These systems confound attribution: gains may come from richer observations or alternative motor tools, while failures may stem from either the policy or an under-specified language interface....

    arxiv.org/abs/2609.11308 · PDF

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