cs.RO · 2026-06-10 · No. 19

Robotics, 2026-06-10.

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

    RoboNaldo: Accurate, Stable and Powerful Humanoid Soccer Shooting via Motion-Guided Curriculum Reinforcement Learning

    Yichao Zhong, Yidan Lu, Yuhang Lu, Tianyang Tang, Haoguang Mai, Yixuan Pan, Tianyu Li, Li Chen, Jingbo Wang, Zhongyu...

    cs.RO · cs.AI

    Elite humanoid soccer shooting requires whole-body stability, high-impulse whole-body interactions, and accuracy to targets. Motion tracking-driven reinforcement learning (RL) provides stability in whole-body movement coordination, but a fixed reference makes it hard to adapt to varied ball positions and strike timings; in contrast, task reward-driven RL struggles to explore and discover valid kicks from scratch. We therefore introduce...

    arxiv.org/abs/2606.11092 · PDF

  2. 02

    Diffusion Forcing Planner: History-Annealed Planning with Time-Dependent Guidance for Autonomous Driving

    Zehan Zhang, Neng Zhang, Yaoyi Li, Jia Cai, Zhiling Wang

    cs.RO · cs.AI

    Learning-based motion planners, despite recent progress, often suffer from temporal inconsistency. Small perturbations across frames can accumulate into unstable trajectories, degrading comfort and safety in closed-loop driving. Several methods attempt to inject history as a static conditioning signal to stabilize outputs, only to induce the planner to copy historical patterns instead of adapting to environment contexts. To address this...

    arxiv.org/abs/2606.11019 · PDF

  3. 03

    Task Robustness via Re-Labelling Vision-Action Robot Data

    Artur Kuramshin, Özgür Aslan, Cyrus Neary, Glen Berseth

    cs.RO · cs.LG

    The recent trend in scaling models for robot learning has resulted in impressive policies that can perform various manipulation tasks and generalize to novel scenarios. However, these policies continue to struggle with following instructions, likely due to the limited linguistic and action sequence diversity in existing robotics datasets. This paper introduces Task Robustness via Re-Labelling Vision-Action Robot Data (TREAD), a scalable...

    arxiv.org/abs/2606.10918 · PDF

  4. 04

    Embodiment-conditioned Generalist Control for Multirotor Aerial Robots

    Orestis Konstantaropoulos, Welf Rehberg, Mihir Kulkarni, Kostas Alexis

    cs.RO · cs.LG

    We present a generalist position control policy capable of controlling arbitrary multirotor configurations of a certain rotor count (e.g., hexarotors or quadrotors) with a single set of network weights. The policy is conditioned on a physics-grounded embodiment descriptor: a mass and inertia-normalized control allocation matrix that captures how mass-normalized motor thrusts generate linear and angular accelerations in the body-frame. To...

    arxiv.org/abs/2606.10857 · PDF

  5. 05

    UniDexTok: A Unified Dexterous Hand Tokenizer from Real Data

    Dong Fang, Youjun Wu, Yuanxin Zhong, Rui Zhang, Yunlong Wang, Xiaosong Jia, Yu-Gang Jiang

    cs.RO · cs.AI · cs.CV

    Dexterous hands are essential for fine-grained manipulation, but their hardware designs vary substantially across embodiments. Differences in kinematics, joint definitions, and degrees of freedom make it difficult to define a shared state representation compared with parallel grippers. As a result, dexterous-hand data remains fragmented and difficult to use for joint training. In this work, we propose the Unified Dexterous Hand Model (UDHM),...

    arxiv.org/abs/2606.10683 · PDF

  6. 06

    Dexterous Point Policy: Learning Point-based Dexterous Hand Policies from Human Demonstrations

    Beomjun Kim, Seong Hyeon Park, Seunghoon Sim, Seungjun Moon, Sanghyeok Lee, Jinwoo Shin

    cs.RO · cs.CV · cs.LG

    Robotic foundation models pre-trained on human demonstration videos have shown promise, but a significant embodiment gap remains when the resulting policies are deployed on real robots. A common remedy is to fine-tune these models on robot-specific demonstrations. However, robot data collection can be prohibitively expensive and time-consuming, which is particularly acute in dexterous manipulation, e.g., teleoperating a multi-fingered hand...

    arxiv.org/abs/2606.10614 · PDF

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