cs.RO · 2026-05-25 · No. 9

Robotics, 2026-05-25.

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

01 — The papers

8 entries
  1. 01

    Any2Any: Efficient Cross-Embodiment Transfer for Humanoid Whole-Body Tracking

    Ming Yang, Tao Yu, Feng Li, Hua Chen

    cs.RO · cs.AI

    Whole-body tracking (WBT) models have become a key foundation for humanoid robots, enabling them to imitate diverse motions with high fidelity. Training such models from scratch requires large-scale data and computation, making rapid deployment on new humanoid platforms costly. This raises a natural question: Can pretrained WBT models transfer across embodiments with minimal adaptation? To answer this question, we propose Any2Any, a paradigm...

    arxiv.org/abs/2605.23733 · PDF

  2. 02

    Sparse Compositional Flow Matching by geometric assembly from motion primitives

    Yan Tang, Yuanbo Tang, Tingyu Cao, Shaolun Huang, Yang Li

    cs.RO · cs.AI

    Embodied trajectories, such as the executable motion sequences of robotic manipulators, underwater vehicles, and mobile robots, are a fundamental output of embodied AI. Modern generative models often treat them as a dense, monolithic signal generated point by point, fitting an intricate high-dimensional posterior while leaving the data's latent structure unmodeled, the same sample inefficiency long identified by the structured generative...

    arxiv.org/abs/2605.23341 · PDF

  3. 03

    6G Communication Networks Enabling Embodied Agents: Architecture and Prototype

    Lipeng Dai, Luping Xiang, Kun Yang

    cs.RO · cs.AI · eess.SP · eess.SY

    Embodied agents, which couple intelligent decision-making with physical actuation in the real world, impose far more stringent and heterogeneous communication requirements than purely software-based agents. While 6G promises sub-millisecond latency, ultra-high reliability, native intelligence, and integrated sensing, systematic studies on how to exploit these capabilities for embodied agent communication remain limited. This article...

    arxiv.org/abs/2605.23263 · PDF

  4. 04

    Autonomous Frontier-Based Exploration with VLM Guidance

    Aarush Aitha, Avideh Zakhor

    cs.RO · cs.AI · cs.CL

    Autonomous robotic exploration of unknown and hazardous environments, a long-standing challenge, can be significantly improved by leveraging the advanced reasoning of Vision-Language Models (VLMs). We introduce a novel exploration pipeline where a VLM performs high-level strategic decision-making, guiding a conventional low-level robotics control stack. At decision points, the robot generates a multimodal prompt with its current map and...

    arxiv.org/abs/2605.23165 · PDF

  5. 05

    Robots That Know What to Ask: Recovering Misaligned Rewards through Targeted Explanations

    Helena Merker, Nick Walker, Andreea Bobu

    cs.RO · cs.AI · cs.HC · cs.LG

    Learning reward functions from demonstrations assumes that demonstrations provide adequate supervision over all features -- or task-relevant aspects of behavior. In practice, demonstrations are often imperfect: humans may under-emphasize certain features due to cognitive load or physical difficulty, or the training regime may fail to sufficiently cover all relevant situations. In either case, important features may be underspecified, leading...

    arxiv.org/abs/2605.22986 · PDF

  6. 06

    Superhuman Safe and Agile Racing through Multi-Agent Reinforcement Learning

    Ismail Geles, Leonard Bauersfeld, Markus Wulfmeier, Davide Scaramuzza

    cs.RO · cs.AI · cs.LG · cs.MA

    Autonomous systems have achieved superhuman performance in isolation or simulation, yet they remain brittle in shared, dynamic real-world spaces. This failure stems from the dominant single-agent paradigm for physical applications, where other actors are ignored or treated as environmental noise, preventing effective coordination. Here we show that multi-agent reinforcement learning provides the essential safety scaffolding required for...

    arxiv.org/abs/2605.22748 · PDF

  7. 07

    Scout-Assisted Planning for Heterogeneous Robot Teams under Partially Known Environments

    Hoang-Dung Bui, Abhish Khanal, Raihan Islam Arnob, Gregory J. Stein

    cs.RO · cs.AI

    Autonomous robot teams navigating partially known environments face costly backtracking when ground robots encounter blocked roads that are only revealed upon physical traversal. We address this with Scout-Assisted Planning, a heterogeneous planning framework in which scouting Unmanned Aerial Vehicles proactively gather environmental information to improve Unmanned Ground Vehicle navigation. To focus scouting on the most consequential edges,...

    arxiv.org/abs/2605.22693 · PDF

  8. 08

    Agentic-VLA: Efficient Online Adaptation for Vision-Language-Action Models

    Ruofan Jin, Zaixi Zhang

    cs.RO · cs.AI · cs.LG

    Vision-Language-Action (VLA) models have emerged as a promising paradigm for robotic manipulation by leveraging pre-trained vision-language representations. However, current VLA training methods suffer from two critical limitations: poor generalization to novel environments and low training efficiency requiring extensive demonstrations. We introduce Agentic-VLA, an agentic training framework that enables VLAs to efficiently adapt online...

    arxiv.org/abs/2605.22896 · PDF

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