cs.RO · 2026-09-25 · No. 124

Robotics, 2026-09-25.

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

01 — The papers

7 entries
  1. 01

    RAPID: Robot Agentic Programming from Demonstrations

    Yuyao Liu, Jiayuan Mao, David Hsu, Leslie Pack Kaelbling, Tomás Lozano-Pérez

    cs.RO · cs.AI · cs.CV

    Coding agents have demonstrated enormous success in solving complex programming problems. To leverage their potential for robot systems, this work introduces Robot Agentic Programming from Demonstrations (RAPID), which automatically generates, verifies, and refines robot programs, given a single visual human demonstration. The iterative agentic loop of code refinement requires several key ingredients: (i) a testable task specification, (ii)...

    arxiv.org/abs/2609.30249 · PDF

  2. 02

    Rolling-WAM: World Action Models with Rolling Imagination

    Yinghua Zhou, Junjie Ye, Yiqi Zhao, Hao Dong, Celina Shiyu Wang, Ruohai Ge, Tingyi Yang, Basile Van Hoorick, Gaurav...

    cs.RO · cs.AI · cs.CV

    World Action Models (WAMs) couple action generation with future visual prediction for robotic manipulation. However, completing the joint video-action denoising process at each replanning cycle incurs substantial latency, delaying action updates and limiting closed-loop responsiveness. We present Rolling-WAM, a formulation that distributes joint denoising across successive replanning cycles. Our method maintains a sliding window of...

    arxiv.org/abs/2609.30247 · PDF

  3. 03

    Coding Agents for Generalized Task and Motion Planning Problems

    Matteo Merler, Bowen Li, Josh Roy, Yichao Liang, Qianwei Wang, Yixuan Huang, Tom Silver

    cs.RO · cs.AI

    Task and motion planning (TAMP) problems remain difficult even with full observability and object-centric states because discrete decisions are tightly coupled to geometric, kinematic, and dynamic constraints. Generalized TAMP addresses this difficulty by exploiting regularities across problem instances to reduce planning effort on new instances. However, existing methods require substantial TAMP-specific engineering. We investigate whether...

    arxiv.org/abs/2609.30233 · PDF

  4. 04

    Underwater C3-JEPA: An Object-Centric Cross-View World Model for ROV Salvage

    Yuncong Yang, Jinlong Li, Yulong Xue, Feng Wu, Chunwen Zhang, Lei Qiao, Xuyang Wang

    cs.RO · cs.AI

    We present Underwater C$^{3}$-JEPA (cross-view, control-conditioned, context-extended), an object-centric multi-view predictive world model for near-field heavy-load underwater ROV salvage. Without contact sensors, it predicts in latent space how the task-object state evolves through contact interaction and under the hydrodynamic lag of the vehicle, from synchronized multi-view RGB observations and vehicle control signals. C$^{3}$-JEPA...

    arxiv.org/abs/2609.30214 · PDF

  5. 05

    World Action Agent: Harnessing VLMs for Robot Manipulation via World Action Rehearsal

    Yehang Zhang, Haojian Huang, Yifan Chang, Jianchong Su, Bohan Zhou, Yingjie Xu, Wosong Chen, Tianhao Zhou, Chenxu...

    cs.RO · cs.AI

    General-purpose vision-language models (VLMs) bring broad knowledge and spatial reasoning to robot manipulation, yet existing systems either use them indirectly, to predict constraints or write programs, or give them a view of the scene rather than a world in which to act. We present World Action Agent (WAA), a multi-agent harness through which VLMs pilot robots with basic tools, making every decision within a visual action workspace. The...

    arxiv.org/abs/2609.29964 · PDF

  6. 06

    MorphIK: Morphology-Conditioned Neural Inverse Kinematics for Unknown Robots

    Lennart Clasmeier, Jan Gerrit Habekost, Cornelius Weber, Stefan Wermter

    cs.RO · cs.AI

    Neural models can learn to generate various solutions to the inverse kinematics problem from data, but are usually limited to a single robot. We present MorphIK, a flow-matching model that solves inverse kinematics for revolute-joint-based kinematic chains it has never seen during training. The model uses a transformer architecture to encode the robot's morphology along with the target pose. This encoding then conditions a flow-matching head...

    arxiv.org/abs/2609.29908 · PDF

  7. 07

    Decoupled Early Exits for Task-Dependent Compute Allocation in Flow-Matching VLAs

    Riccardo Andrea Izzo, Rimvydas Rubavicius, Gianluca Bardaro, Subramanian Ramamoorthy, Matteo Matteucci, Alessandro Suglia

    cs.RO · cs.LG

    Flow-matching Vision-Language-Action (VLA) models have emerged as a potential solution for generalist robot control, designed by combining a pretrained Vision-Language Model (VLM) backbone with an action expert that generates continuous robot actions. While these models exhibit impressive capabilities, due to their very high number of parameters, their computational requirements are often prohibitive for robotics control. To mitigate these...

    arxiv.org/abs/2609.29382 · PDF

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