cs.RO · 2026-08-25 · No. 95

Robotics, 2026-08-25.

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

    Act with Intent: Distilling Behavior Intent for Vision-Language-Action Models

    Sangoh Lee, Sangwoo Mo, Wook-Shin Han

    cs.RO · cs.AI · cs.CV

    Vision-Language-Action (VLA) models can turn multimodal context into robot actions, but their action decoders are still trained largely by behavior cloning. This supervises which motor command was demonstrated while leaving implicit the local objective served by the behavior under the instruction. Future-based supervision enriches action learning with frames, latent observations, trajectories, or motion representations, but these signals...

    arxiv.org/abs/2608.23478 · PDF

  2. 02

    Reward-Free Continual Adaptation for Resilient Space Robots

    Andrej Orsula, Miguel Olivares-Mendez, Carol Martinez

    cs.RO · cs.AI · cs.LG

    Space robots operate in extreme environments where hardware degradation can critically compromise traditional control strategies. While continual reinforcement learning offers a promising mechanism for online adaptation, it inherently requires access to a reward signal during deployment. However, precise reward computation in space is often infeasible due to the lack of external tracking systems and the overall complexity of the environment....

    arxiv.org/abs/2608.23452 · PDF

  3. 03

    Think Only When Needed: Prompt-Authority Control for Selective Slow-Path Intervention in Vision-Language-Action Manipulation

    Zhiruo Zhou, Zelin Li, Xiwen Chen, Jiazhuo Li, Chenwei Wang, Huiming Chen, Xiaojun Zhu

    cs.RO · cs.AI · cs.CV

    Retrieval can efficiently and effectively augment a frozen vision--language--action (VLA) policy without retraining, yet retrieved text becomes a control intervention once it enters the executed prompt. In a matched audit, raw appended text reduces mean success from 92.47\% to 3.00\%, while meaningful and length-matched meaningless appends both fail on all 500 states. This result identifies \emph{prompt-form collapse}: changing the...

    arxiv.org/abs/2608.23224 · PDF

  4. 04

    Pointing-VLA: Typed Spatial Grounding Interfaces for Vision-Language-Action Manipulation

    Xiwen Chen, Zelin Li, Zhiruo Zhou, Huiming Chen, Chenwei Wang, Xiaojun Zhu

    cs.RO · cs.AI · cs.CV

    Vision-language-action (VLA) models often expose spatial grounding through autoregressive text coordinates or opaque action tokens, creating brittle interfaces between multimodal reasoning and robot execution. We present Pointing-VLA, a typed hidden-state spatial readout built on Embodied-R1. Geometry-specific heads predict normalized points, object-functional grounding (OFG) heatmaps, and visual trajectories without serializing geometry as...

    arxiv.org/abs/2608.23138 · PDF

  5. 05

    Shaping the Evolutionary Dynamics of Robot Morphology via Adaptive Control Learning

    Junru Song, Yang Yang, Yaqing Xu, Ying Wen, Wei Peng, Guozhen Li, Wei'en Zhou, Wen Yao

    cs.RO · cs.AI

    Robot co-design via bi-level optimization couples within-lifetime controller learning for fitness evaluation with cross-generational morphological evolution. Prior work has established that well-adapted morphology facilitates faster control learning, a property termed morphological intelligence. Yet how control learning reciprocally shapes morphological evolution remains unexplored. This paper examines both directions for a holistic account...

    arxiv.org/abs/2608.23100 · PDF

  6. 06

    Triplet2Track: A Hierarchical System with Object-Centric Representations for Reliable Long-Horizon Manipulation

    Jianxiang Liu, Gaojing Zhang, Chuan Wen, Qipeng Liu, Yuxuan Zhao, Ning Guo, Wenzhao Lian

    cs.RO · cs.AI

    Ensuring reliability in uncertain environments remains difficult for long-horizon robotic manipulation. End-to-end VLA models are data-heavy and opaque, making diagnosis and verification difficult. Hierarchical pipelines are more interpretable, but their plans are often weakly grounded in observations, weakly aligned with low-level actions, and computed without online feedback, leading to open-loop behavior and hallucinations. To address...

    arxiv.org/abs/2608.22800 · PDF

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