cs.RO · 2026-09-28 · No. 127

Robotics, 2026-09-28.

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

01 — The papers

3 entries
  1. 01

    Guiding End-to-End Driving Models with Endpoint-Constrained Trajectory Optimization

    Brayden Zhang, Mahsa Golchoubian, Igor Gilitschenski, Boris Ivanovic, Kashyap Chitta

    cs.RO · cs.AI · cs.CV · cs.LG

    End-to-end driving policies are commonly trained through open-loop behavior cloning, yet they must ultimately operate in closed-loop when deployed on a vehicle, creating a fundamental mismatch between training and execution. Beyond the commonly studied effects of covariate shift and causal confusion, we identify a complementary factor for this open-loop/closed-loop gap: waypoint-based supervision and displacement metrics do not ensure that...

    arxiv.org/abs/2609.31383 · PDF

  2. 02

    Towards VLA-Dreamer: Refining VLA Behavior Using World Models

    Parsa Mastouri Kashani, Jan-Gerrit Habekost, Stefan Wermter

    cs.RO · cs.AI

    Vision-Language-Action models (VLAs), while showing strong potential for robot control, require massive amounts of high-quality imitation learning data. Moreover, the absence of an explicit world model casts further doubt on their control capabilities. In this concept paper, we propose a novel architecture that addresses sample efficiency in VLAs by training a predictive world model on the embedding space of the VLA's vision encoder. We...

    arxiv.org/abs/2609.31313 · PDF

  3. 03

    Precision at Speed: Sample-Efficient Online Model-Based Reinforcement Learning for Hydraulic Excavator Control

    Claudio Canales, Fang Nan, Marco Hutter, Javier Ruiz-del-Solar

    cs.RO · cs.LG · eess.SY

    Precise, high-speed control remains challenging for robots with complex actuation dynamics. Learning directly on hardware is further constrained by the cost of real-world interaction. We present an online model-based reinforcement learning framework that learns a probabilistic dynamics ensemble model from scratch for sampling-based model predictive control. A precision-gated contouring objective conditions the progress reward on path...

    arxiv.org/abs/2609.31025 · PDF

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