cs.RO · 2026-06-23 · No. 32

Robotics, 2026-06-23.

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

    AutoDex: An Automated Real-World System for Dexterous Grasping Data Collection

    Mingi Choi, Gunhee Kim, Jisoo Kim, Taeksoo Kim, Taeyun Ha, Jongbin Lim, Hanbyul Joo

    cs.RO · cs.LG

    Learning robust dexterous grasping requires real-world data that records the physical outcomes of grasp attempts. Such data is hard to obtain at scale: teleoperation yields valid physical outcomes but is slow and operator-biased, while simulation-based generation is cheap and scalable but cannot certify contact validity. A natural solution is to generate candidate grasps and verify them on real hardware, but this scales only if the entire...

    arxiv.org/abs/2606.23689 · PDF

  2. 02

    CoorDex: Coordinating Body and Hand Priors for Continuous Dexterous Humanoid Loco-Manipulation

    Sikai Li, Shuning Li, Zhenyu Wei, Yunchao Yao, Chenran Li, Mingyu Ding

    cs.RO · cs.AI · cs.LG

    Humanoid loco-manipulation is often simplified into a stop-and-go process: walking to an object, stopping to manipulate it, and then resuming locomotion. It also commonly relies on low degree-of-freedom (DoF) end effectors that behave like an open-close grasp primitive. We introduce CoorDex, a learning pipeline that converts high-dimensional body and dexterous hand control into coordinated latent residual control, enabling high-DoF dexterous...

    arxiv.org/abs/2606.23680 · PDF

  3. 03

    RECALL: Recovery Experience Collection for Active Lifelong Learning in Vision-Language-Action Models

    Ulas Berk Karli, Tesca Fitzgerald

    cs.RO · cs.AI · cs.LG

    Vision-Language-Action (VLA) models are commonly fine-tuned through passive imitation learning, where additional demonstrations are collected for tasks where the policy performs poorly. This approach incurs several downsides: it requires the robot to fail before data collection is triggered, provides little guidance about which states require supervision, and wastes demonstrator effort on redundant parts of the task where the policy already...

    arxiv.org/abs/2606.23617 · PDF

  4. 04

    A Generative Model for Closed-Loop Microsimulation of Signalized Intersections

    Yash Ranjan, Rahul Sengupta, Anand Rangarajan, Sanjay Ranka

    cs.RO · cs.AI

    Traffic microsimulators rely on hand-crafted behavior models that reproduce aggregate flow but miss the heterogeneous interactions between vehicles at signalized intersections. Learned trajectory predictors capture richer interactions but are short-horizon and tend to be unstable when run in closed loop. We present Enactor, an actor-centric generative model for closed-loop intersection microsimulation. The model focuses on vehicles;...

    arxiv.org/abs/2606.23588 · PDF

  5. 05

    DVL-DeepONet: A Physics-Guided Operator Learning for Resilient Underwater Navigation

    Arup Kumar Sahoo, Itzik Klein

    cs.RO · cs.AI

    Autonomous Underwater Vehicles (AUVs) rely heavily on the fusion of inertial sensors and Doppler velocity logs (DVLs) for navigation. In standard autonomous navigation systems, the DVL measures four beam velocities, thereby enabling the estimation of the AUV velocity vector. However, during real-world missions, the DVL may receive noisy or incomplete beam measurements due to marine obstacles, seabed reflections, or environmental disturbances....

    arxiv.org/abs/2606.23502 · PDF

  6. 06

    SkyJEPA: Learning Long-Horizon World Models for Zero-Shot Sim-to-Real Control of Quadrotors

    Pratyaksh Rao, Wancong Zhang, Randall Balestriero, Yann LeCun, Giuseppe Loianno

    cs.RO · cs.LG

    Accurate dynamics models are critical for informed decision-making in robotic systems, particularly for agile aerial vehicles operating under uncertainty. Neural network dynamics models are attractive for capturing complex nonlinear effects, but existing predictive approaches struggle with long-horizon forecasting because their autoregressive rollout mechanism amplifies errors over time. Joint Embedding Predictive Architectures (JEPAs) offer...

    arxiv.org/abs/2606.23444 · PDF

  7. 07

    AdaReP:Adaptive Re-Planning under Model Mismatch for Neural World-Model Predictive Control

    Yutian Cheng, Xiaojian Ma, Xianhao Wang, Min Yang, Rongpeng Su, Hangxin Liu, Xi Chen, Shuai Li, Qing Li

    cs.RO · cs.AI

    Neural world models coupled with model predictive control (MPC) replan at every environment step to bound accumulated prediction error, but this incurs substantial computational overhead. Reusing a cached plan reduces this overhead, yet its effectiveness depends on how prediction mismatch propagates through the local dynamics. We analyze this trade-off with a perturbation-based dynamic-regret framework and show that stale-plan penalties scale...

    arxiv.org/abs/2606.23079 · PDF

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