cs.RO · 2026-05-28 · No. 11

Robotics, 2026-05-28.

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

01 — The papers

7 entries
  1. 01

    Beyond Binary: Sim-to-Real Dexterous Manipulation with Physics-Grounded Contact Representation

    Jiahe Pan, Stelian Coros, Jitendra Malik, Toru Lin

    cs.RO · cs.AI · cs.LG

    A primary bottleneck in contact-rich manipulation is the difficulty of collecting real-world data. Sim-to-real reinforcement learning offers a scalable alternative, but the simulation-reality gap prevents information-dense modalities like touch from being effectively used. Existing sim-to-real methods often mitigate this gap by simplifying tactile data into coarse low-dimensional features -- sacrificing the richness required for complex...

    arxiv.org/abs/2605.28812 · PDF

  2. 02

    How VLAs Fail Differently: Black-Box Action Monitoring Reveals Architecture-Specific Failure Signatures

    Krishnam Gupta

    cs.RO · cs.LG

    We discover that VLA architectures fail in fundamentally different, predictable ways at the motor-command level. Running VQ-BeT, Diffusion Policy, and ACT on identical evaluation protocols (n=450 episodes across PushT and ALOHA 14-DOF bimanual manipulation), we find: (1) direction reversal rate is a universal failure predictor across all three architectures (AUROC=0.93, 0.79, 0.91; p<0.001); (2) jerk monitoring is predictive only for...

    arxiv.org/abs/2605.28726 · PDF

  3. 03

    SARAD: LLM-Based Safety-Aware Hybrid Reinforcement Learning with Collision Prediction for Autonomous Driving

    Kangyu Wu, Peng Cui, Guoxi Chen, Ya Zhang

    cs.RO · cs.AI · cs.LG · eess.SY

    Ensuring both safety and efficiency in decision-making for autonomous driving systems remains a fundamental challenge. Traditional Deep Reinforcement Learning (DRL) suffers from unsafe random exploration and slow convergence, while Large Language Models (LLMs) demonstrate inherent latency in real-time inference operations. To address these limitations, this paper proposes SARAD, a novel safety-aware hybrid framework that synergizes LLMs and...

    arxiv.org/abs/2605.28583 · PDF

  4. 04

    SPRINT: Efficient Spectral Priors for Humanoid Athletic Sprints

    Yantong Wei, Kaihong Huang, Hainan Pan, Jiawei Luo, Jiawei Zhou, Ziyan Mai, Zhiwen Zeng, Yaonan Wang, Huimin Lu

    cs.RO · cs.LG

    The pursuit of humanoid athletic sprints is hindered by a scarcity of humanoid-viable kinematic reference data and the inability of existing frameworks to maintain stability during sprints. To overcome these limitations, we introduce SPRINT, a novel framework driven by efficient, frequency-adaptive spectral priors. By characterizing the fundamental periodicity of human locomotion in the frequency domain using a reference library of five...

    arxiv.org/abs/2605.28549 · PDF

  5. 05

    Tactile-Proprioceptive Sensor Fusion for Contact Wrench Estimation in Whole-Body Physical Human-Robot Interaction

    Junha Min, Junghyeon Ma, Jiwung Kwon, Sunggyu Bae, Joohyung Kim, Kyungseo Park

    cs.RO · cs.LG

    Direct physical guidance is a natural means of teaching and interacting with robots, and robotic skins make a key contribution by enabling sensitive contact sensing and localization. This paper presents a tactile-proprioceptive sensor fusion framework for natural physical human-robot interaction. Tactile cues from pneumatic skin pads serve as contact indicators that bypass the ambiguity between frictional residues and applied external forces,...

    arxiv.org/abs/2605.28412 · PDF

  6. 06

    Identifying Explicit Parsimonious Piece-wise Polynomial Relationships in Industrial time-series: Application to manipulator robots

    Mazen Alamir, Sacha Clavel

    cs.RO · cs.AI

    This paper addresses the problem of identifying parsimonious explicit piece-wise polynomial relationships that might involve a relatively large number of raw features. The algorithm leverages a recently proposed identification algorithm that yields parsimonious implicit relationships enabling to derive normality characterization in the context of anomaly detection and localization. The algorithm proposed in this paper goes a step further by...

    arxiv.org/abs/2605.28320 · PDF

  7. 07

    ProgVLA: Progress-Aware Robot Manipulation Skill Learning

    Seungsu Kim, Jinyoung Choi, Seungmin Baek, Jean-Michel Renders

    cs.RO · cs.LG

    We present ProgVLA, a compact vision-language-action (VLA) model designed for reliable robot manipulation under tight compute and memory budgets. The model specifically focuses on efficiently processing long multi-modal sequences by maintaining an explicit representation of task progress over extended horizons. To this end, ProgVLA integrates two key components. First, a multi-modal encoder with a two-stage Perceiver resampling scheme...

    arxiv.org/abs/2605.28231 · PDF

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