cs.RO · 2026-08-23 · No. 93

Robotics, 2026-08-23.

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

    Towards Professional Tennis Styles for Humanoid Robots with Adaptive Motion Planning and Tracking

    Tao Huang, Ruofei Liu, Xuchen Tang, Xinyin Zhang, Junli Ren, Huayi Wang, Feiyu Jia, Yukai Qi, Kangning Yin, Weishuai...

    cs.RO · cs.AI

    Humanoid robots have recently demonstrated promising capabilities in real-world ball sports. However, achieving professional motion styles while maintaining strong task performance remains challenging. In this work, we propose AdaPT, an Adaptive Motion Planning and Tracking framework that learns professional tennis serving and rally styles directly from broadcast videos. This hierarchical design is motivated by the key insight that the...

    arxiv.org/abs/2608.20087 · PDF

  2. 02

    Evidence-Gated Task and Motion Planning with Vision-Language Models

    Tsunehiko Tanaka, Matthew Stephenson, Alistair Macvicar, Edgar Simo-Serra

    cs.RO · cs.AI

    Robots executing long-horizon manipulation tasks from natural-language instructions must reason about both semantic task structure and geometric feasibility. However, under partial observability, the availability of goal-relevant objects may be uncertain. In such cases, approaches that combine Vision-Language Models (VLMs) with Task and Motion Planning (TAMP) may generate subgoals that rely on the VLM's prior knowledge without observational...

    arxiv.org/abs/2608.20084 · PDF

  3. 03

    CoToGrasp: Contact-Topology-Conditioned Dexterous Grasp Synthesis via Canonical Workspace Learning

    Julien Merand, Boris Meden, Liming Chen, Mathieu Grossard

    cs.RO · cs.AI

    Current dexterous grasp planners primarily optimize for physical stability, focusing on whether an object can be grasped rather than how it should be grasped to support downstream functional tasks. However, conditioning grasp synthesis on specific human grasp taxonomies typically requires prohibitively expensive, object-annotated datasets. To address these limitations, we propose CoToGrasp, a novel generative framework that synthesizes...

    arxiv.org/abs/2608.19776 · PDF

  4. 04

    GOAG: Generative and Object-Agnostic Grasp Planner for Dexterous Robotic Manipulation

    Julien Merand, Boris Meden, Mathieu Grossard, Liming Chen

    cs.RO · cs.AI

    Multifingered grasping is a crucial robotic skill, but current deep-learning grasp planners often struggle to generalize to new objects because they are trained on limited, object-specific datasets. We introduce a fundamentally different approach, grounded in the observation that the gripper and the object share identical surface geometry at their mutual contact points. We propose GOAG: Generative and Object-Agnostic Grasp Planner for...

    arxiv.org/abs/2608.19759 · PDF

  5. 05

    Fine-Tuning VLAs with Self-Demonstrated Generative Control for Multi-Task Manipulation

    Prachi Garg, Steve Xing, Prahit Yaugand, Saurabh Gupta, Derek Hoiem

    cs.RO · cs.CV · cs.LG

    State-of-the-art vision-language-action (VLA) models such as $π_{0.5}$ exhibit strong semantic understanding, instruction following and task behavior. However, when deployed on new robots, even minor mismatches in hardware configuration relative to pretraining can cause severe performance drops. Finetuning the VLA on in-domain expert data from the new embodiment improves performance on the expert task but leads to a loss in its original...

    arxiv.org/abs/2608.19490 · PDF

  6. 06

    SCAPE: Scenario-Conditioned Simulation-Augmented Policy Evaluation

    Dijie Zhu, Seunghun Oh, Ruopeng Huang, Zhiyu Huang, Jiaqi Ma, Chen Tang

    cs.RO · cs.AI · cs.LG

    Reliable performance evaluation is a central bottleneck for deploying robot-learning policies in real-world conditions. Real-world testing is faithful but costly and difficult to scale, whereas simulation-based testing scales easily but is inevitably biased by the sim-to-real gap. Existing simulation-augmented methods combine limited real-world rollouts with abundant simulation proxies, but focus on performance averaged over initial...

    arxiv.org/abs/2608.19425 · PDF

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