cs.RO · 2026-08-20 · No. 90

Robotics, 2026-08-20.

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

01 — The papers

5 entries
  1. 01

    ADEPT: Accelerating Dexterity via Pre-Training and Post-Training using Reinforcement Learning

    Jayjun Lee, Jessica Yin, Asif Rana, Nicholas Blauch, Sam Mady, Mohak Bhardwaj, Nima Fazeli, Nathan Ratliff, Karl Van...

    cs.RO · cs.AI

    We introduce Accelerating Dexterity via Pre-Training (ADEPT), a large-scale reinforcement learning (RL) framework for learning sim-to-real transferable dexterity across high degree-of-freedom (DoF) robot embodiments that can solve long-horizon tasks directly from raw visuo-tactile perception. ADEPT pretrains a dexterous policy on a generic object reposing task, then post-trains downstream policies with this pretrained behavior as a prior....

    arxiv.org/abs/2608.19182 · PDF

  2. 02

    DA-WAM: Decision-Aligned Future Latents for Driving World Models

    Ruiguo Zhong, Benshan Ma, Xiaolong Chen, Lang Zhang, Mingyue Feng, Yaonong Wang, Pei Liu, Jun Ma

    cs.RO · cs.AI

    Anticipating how scenes evolve under ego actions is fundamental to safe autonomous driving, yet the full potential of world models for decision-making remains unrealized. The critical challenge lies in ensuring that future modeling is not merely predictive, but decision-informative: the predicted future must directly shape which trajectory is selected. Existing approaches decouple future representation learning from planning optimization, or...

    arxiv.org/abs/2608.19085 · PDF

  3. 03

    Orienteering Problem with Uncertain Time-Varying Rewards: Framework and Benchmark for Everyday Service Robotics

    Masafumi Endo, Kohei Honda, Yuu Jinnai, Ryo Yonetani

    cs.RO · cs.AI

    We present the orienteering problem with uncertain time-varying rewards (OP-UTVR), a novel variant of the orienteering problem (OP). While most existing OP formulations assume rewards to be known in advance, practical applications involve uncertain and time-varying rewards, as with shifting customer demand for delivery agents. OP-UTVR relaxes this assumption by allowing agents to estimate reward dynamics from observations and forecast future...

    arxiv.org/abs/2608.18672 · PDF

  4. 04

    Progressive Experience Fusion for Multi-Task World Model Control in Endovascular Navigation

    Harry Robertshaw, Maxence Boels, Nikola Fischer, Sebastien Ourselin, Christos Bergeles, Alejandro Granados, Thomas C Booth

    cs.RO · cs.LG

    Autonomous endovascular navigation could support the delivery of mechanical thrombectomy to underserved areas, but controllers must navigate long, multi-stage paths across varying vascular anatomies. This study investigates Progressive Experience Fusion (PEF) to train a multi-task TD-MPC2 controller. We additionally evaluate a heuristic that changes the Model Predictive Path Integral planning horizon using residual action-sequence dispersion,...

    arxiv.org/abs/2608.18647 · PDF

  5. 05

    The Embodiment Gap in Robot Foundation Models

    Yukiyasu Domae, Keisuke Shirai, Hanbit Oh, Ryoichi Nakajo, Tomohiro Motoda, Koshi Makihara, Masaki Murooka, Takuma...

    cs.RO · cs.LG

    Robot foundation models (RFMs), including vision-language-action (VLA) policies, are often discussed through a scaling view: more data, larger models, and broader benchmarks should improve generalization. In robotics, however, a model can generalize while work still remains before it can run on a robot with a particular body. The work required differs across methods and target robots, and those differences affect practical deployment. We call...

    arxiv.org/abs/2608.18433 · PDF

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