cs.RO · 2026-08-11 · No. 81

Robotics, 2026-08-11.

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

01 — The papers

6 entries
  1. 01

    Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning

    Yapeng Liu, Yuanzhao Zhai, Bo Ding, Huaimin Wang, Lin Wang

    cs.RO · cs.AI

    Physically consistent motion planning remains a fundamental challenge in embodied AI, as generated trajectories must strictly conform to real-world execution dynamics. While latent world models offer a promising approach by predicting these dynamics, existing methods learn unconstrained future representations where absorbed physics remains implicit. Therefore, they fail to form reusable physical knowledge, which compromises reliability in...

    arxiv.org/abs/2608.09876 · PDF

  2. 02

    Agentic Harnesses: LLM-Driven Verification Layers for Robot Autonomy

    Rohan Bhagra, Mahantesh Halapannavar, Uddhav Bhattarai

    cs.RO · cs.AI

    Advances in advanced artificial intelligence tools have sparked research in robot autonomy, but the development of such systems has largely focused on execution rather than verifying the feasibility actions planning models propose. Like general-purpose LLMs, robotics planning models carry risks: biased toward user-specified goals, they may suggest actions misaligned with scientific ethics, they may be unsafe due to an inability to "remember"...

    arxiv.org/abs/2608.09857 · PDF

  3. 03

    RynnValue: Scaling Robotic Value Foundation Models with Temporal Distance

    Dongchi Huang, Hongyin Zhang, Bohan Hou, Siteng Huang, Zhian Su, Hang Guo, Tong Lu, Zhaofeng Xu, Jiahao Tang,...

    cs.RO · cs.CV · cs.LG

    General-purpose reward models are increasingly the bottleneck for scaling robot learning, yet the recipe for learning value-related capabilities from large-scale heterogeneous corpora remains underexplored. Existing approaches tie supervision to task-internal anchors such as preferences or normalized progress, none of which transfer cleanly across embodiments and data sources. We introduce RynnValue, an open-source value foundation model for...

    arxiv.org/abs/2608.09853 · PDF

  4. 04

    Predictive safety filter enhanced curriculum learning control for efficient vehicle dynamics controller

    Baocong Zhang, Siliang Lu, Chenyang Li

    cs.RO · cs.AI

    Recent advances in learning-based control have enabled impressive achievements in solving complex control problems in various domains. However, since learning-based control may not be able to realize safety-guaranties, it is of great importance to enhance safety and robustness while maintaining good performances. Take vehicle motion \& dynamics control as an example, in order to overcome the pain points of traditional methods such as heavy...

    arxiv.org/abs/2608.09653 · PDF

  5. 05

    SAFE-CHEM: Uncertainty-Aware Policy Switching for Robust Robotic Chemistry

    Laura Jones, Shazil Shahzad, Ayesha Sana, Gabriella Pizzuto

    cs.RO · cs.LG

    The deployment of autonomous robotic systems in chemistry laboratories is accelerating experimental workflows and providing the foundational data for AI-driven scientific discovery. However, despite the success of data-driven methods in acquiring dexterous skills, safety remains a primary barrier to their deployment in high-risk domains, such as early-stage materials chemistry experiments. Specifically, learning-based policies frequently...

    arxiv.org/abs/2608.09303 · PDF

  6. 06

    WorldSimProbe: Diagnosing Simulator Faithfulness in Action-Conditioned World Models for Embodied Manipulation

    Peterson Co, Sicheng Hu, Chunxuan Jiao, Hongyang Cheng, Yulin Luo, Yijie Xu, Sixiang Chen, Zhongxia Zhao, Zihao...

    cs.RO · cs.AI

    Action-conditioned world models (ACWMs) promise to provide embodied AI with scalable predictive simulators for planning, policy evaluation, and data generation. Realizing this promise requires precise action-conditioned transitions rather than merely plausible outputs. Yet their applicability remains difficult to establish because prevailing evaluations emphasize visual quality, task outcomes, or coarse rollout-level responsiveness without...

    arxiv.org/abs/2608.09298 · PDF

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