cs.RO · 2026-07-29 · No. 68

Robotics, 2026-07-29.

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

    $π\mathbf{R}^2$: Reactive Real-time Flow Policies

    Sungjae Park, Shubham Tulsiani

    cs.RO · cs.AI · cs.LG

    Generalist manipulation policies increasingly take the form of action-chunking flow policies built on large pretrained backbones. Such chunks run open-loop, so the policy cannot react to sensory input arriving mid-execution, sacrificing \emph{reactivity}. Replanning more often would restore it, but the perception-to-action pipeline (a large backbone plus multiple denoising steps) is too slow: this \emph{latency} forbids frequent replanning...

    arxiv.org/abs/2607.26055 · PDF

  2. 02

    Physics-Aware End-to-End Deep Reinforcement Learning for Quadcopter Control with Actuator Dynamics

    Ya-Chia Shen, Woei-Leong Chan

    cs.RO · cs.LG · eess.SY

    Unmanned aerial vehicles (UAVs), particularly quadcopters, present unique challenges for autonomous control due to their underactuated dynamics: only four available control inputs must govern six degrees of freedom. This paper investigates a physics-aware, end-to-end deep reinforcement learning (DRL) approach that acts directly on low-level body inputs, total thrust and body torques $(T, τ_x, τ_y, τ_z)$, and closes the loop through a...

    arxiv.org/abs/2607.25985 · PDF

  3. 03

    SAM3D-Guided Object-Centric Representation Alignment for Vision-Language-Action Models

    Zonghe Liu, Shanyuan Jie, Xiaoquan Sun, Chen Cao, Zetian Xu, Zongsheng Liu, Jiayu Chen

    cs.RO · cs.AI

    Vision-Language-Action (VLA) models have shown strong potential for general robot manipulation, but most existing models rely on 2D visual-language backbones and lack fine-grained 3D understanding of target objects, especially under occlusion, pose variation, scale changes, and precise spatial interaction. We propose an object-centric 3D representation alignment framework built upon $π_0$, using SAM3D as a frozen 3D teacher to provide...

    arxiv.org/abs/2607.25912 · PDF

  4. 04

    HiFi-UMI: Learning Deployable Manipulation Policies from High-Fidelity UMI Data Alone

    Simple AI, :, Yuteng Wei, Jinming Ma, Jiawei Wang, Weitao Zhou, Yushen Zuo, Ke Rui, Minglei Li, Jinhao Zhang,...

    cs.RO · cs.CV · cs.LG

    Learning deployable manipulation policies is bottlenecked by the scarcity of data that is both high-fidelity and scalable. Real-robot teleoperation is accurate but costly to scale; robot-free UMI capture scales readily, and current practice uses the resulting data mainly for pre-training, adding a small real-robot "anchor" at post-training. We ask whether raising the fidelity of robot-free UMI data, rather than shrinking the real-robot...

    arxiv.org/abs/2607.25895 · PDF

  5. 05

    Shared Voxel-Map-Based Cooperative Indoor UAV Guidance with a Multi-Agent Soft Actor-Critic Controller

    Thomas Hickling, Dylan Wynne, Yu Su, Nabil Aouf

    cs.RO · cs.AI · cs.LG

    This paper presents a cooperative indoor UAV guidance framework that combines a shared voxel-map world model with a multi-agent Soft Actor-Critic (MASAC) controller. Multiple drones fuse 360 LiDAR observations into a common world-frame occupancy map, which is converted into a compact bird's-eye-view (BEV) representation and provided to each agent as an ego-aligned local crop. This integrate-in-world, act-in- ego design enables consistent...

    arxiv.org/abs/2607.25728 · PDF

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