cs.RO · 2026-09-02 · No. 103

Robotics, 2026-09-02.

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

    Facet-0: A Robotic Foundation Model for Contact-Rich Precise Manipulation

    Haoyuan Deng, Haichao Liu, Wenkai Guo, Yuan Ling, Zaijia Yang, Yuanjiang Xue, Haosheng Sun, Liangzi Wang, Ziwei Wang

    cs.RO · cs.LG

    Real-world robotic assembly at sub-millimeter tolerances demands spatial precision, compliant interaction, and robustness to contact failures. We present Facet-0, a robotic foundation model that predicts and values the contact consequences of its actions. Facet-0 unifies multimodal representation learning and reinforcement learning (RL) post-training around a joint action-wrench proposal: a causal wrench history is aligned with...

    arxiv.org/abs/2609.01596 · PDF

  2. 02

    Does Imitation Learning Preserve Temporal Robustness in Dexterous Manipulation? An Expert-Learner Comparison Across Task Execution Speeds

    Clinton Enwerem, John S. Baras, Calin Belta

    cs.RO · cs.LG

    Dexterous manipulation policies learned by imitation are typically evaluated for robustness to variation in scenes, objects, or instructions, but their performance across task execution speeds is less often examined. This leaves open how much temporal robustness a learner retains relative to the expert it imitates. We compare an expert and learner under the same task conditions, initial-condition draws, and speedup factors. We instantiate the...

    arxiv.org/abs/2609.01453 · PDF

  3. 03

    Evaluating Multimodal LLMs as Generalist Vision-Language-Action Agents for Drone Control: Commanding, Approaching, Tracking and Searching

    Jaewoo Park, Minyoung Lee, Sukmin Seo, Moonbin Yim, Hyunwook Yoon, Dohoon Ryu, Daehee Kim, Myungseo Song, Jihyuk...

    cs.RO · cs.AI

    Multimodal Large Language Models (MLLMs) are strong perceivers of images and video. We ask how far that reach extends into acting: dropping an MLLM directly into a drone's control loop, with its entire action space declared solely in the prompt. Recent systems approach this setting but increasingly narrow the model's decision-making. We widen it back. We introduce DroneCATS-Agent, an architecture where the MLLM is a swappable component, and...

    arxiv.org/abs/2609.01404 · PDF

  4. 04

    Scalable Rao-Blackwellized Online Planning for High-Dimensional POMDPs

    Jiho Lee, Nisar Ahmed, Kyle Hollins Wray, Zachary Sunberg

    cs.RO · cs.AI

    Online planning under uncertainty remains a fundamental challenge for robotic systems operating in partially observable environments with high-dimensional state spaces. While sampling-based POMDP solvers enable approximate decision-making in large or continuous domains, their performance degrades as belief dimensionality increases due to the high variance inherent in Monte Carlo-based estimation. In this work, we extend the Rao-Blackwellized...

    arxiv.org/abs/2609.01351 · PDF

  5. 05

    EmbodiedSkills: A Unified Framework for Orchestrating, Training, and Deploying VLA Agents

    Wei Wang, Wenqiao Zhang, Yutong Lin, Yuqian Yuan, Tianwei Lin, Jinhao Mao, Zhenxuan Fan, Mingjian Gao, Yang Dai,...

    cs.RO · cs.AI

    Vision-language-action (VLA) models map visual observations and language instructions directly to robot actions, but long-horizon tasks require more than action prediction. An agent must coordinate perception, planning, execution, progress verification, and recovery as the physical state evolves. An action prediction or a model-generated skill decision does not, by itself, guarantee that the proposed operation is valid in the current state or...

    arxiv.org/abs/2609.01281 · PDF

  6. 06

    DNC-IMM: Early Lane-Change Intention Recognition via Neural Calibration Based on Driving Context Information

    Woong-Chan Byun, Seung-Hyun Kong

    cs.RO · cs.AI

    Early recognition of lane-change intention is essential for proactive decision-making in autonomous driving and advanced driver assistance systems. This paper proposes a Dual Neural-Calibrated Interacting Multiple Model (DNC-IMM) that improves adaptability to driving context while preserving the probabilistic structure and interpretability of a conventional IMM. The proposed method encodes driving-context information, including target-vehicle...

    arxiv.org/abs/2609.01120 · PDF

  7. 07

    Accelerating Reinforcement Learning via MPC Solver-Gradient Guidance for Weights-varying MPC

    Baha Zarrouki, Arslan Thobani, Jasper Hoffmann, Mattia Piccinini, Rudolf Reiter, Felix Jahncke, Sébastien Gros,...

    cs.RO · cs.LG · eess.SY

    In Model Predictive Control (MPC), cost-function weights shape closed-loop behavior, yet changing conditions often make fixed parametrizations suboptimal and motivate context-dependent online adaptation. Learning such policies is difficult because behavior depends implicitly on numerical MPC solutions, producing nonlinear, potentially nonsmooth, long-horizon dependencies on policy parameters. This creates a bias-variance tradeoff:...

    arxiv.org/abs/2609.01061 · PDF

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