[论文] Pushing Dexterity and Safety for In-Hand Object Pose Reaching That Wor...

论文概要

研究领域: 机器人 / 灵巧操作 作者: Ian Chuang, Hiya Shah, Vamsi Chilamkur, Iman Soltani, Claire Tomlin 发布时间: 2026-10-27 arXiv: 2610.37652

中文摘要

多指灵巧操作是机器人领域的下一个前沿，但学到的手部技能仍局限于简单抓取或基本的重复动作。随着机器人尝试更复杂的hand内操作，安全性（如不掉落物体）变得至关重要，但很少被量化评估。本文研究hand内物体姿态到达任务——要求将物体移动到手指可达范围内的几乎任意位置和朝向，通过长序列连续目标完成。团队在LEAP手上训练策略，无需人类数据，关键洞察包括：通过生成的抓取数据集实现多样化重置和目标采样、自适应摩擦课程学习、精细的系统辨识。策略零样本迁移到真实手部，使用运动捕捉或单个腕部深度相机。在此之上，团队添加了一个可学习残差安全滤波器，可调保守程度，大幅减少了物体掉落。在13.3小时真实机器人评估中，基于状态的策略在2cm和0.4rad容差内达到95.2%的目标，滤波器将掉落减少75%同时仍达到91.5%目标。策略还能旋转任意姿态下的灯泡、到达分布外目标、承受快速手臂运动和扰动。

原文摘要

Dexterous manipulation with multi-fingered hands is the next frontier of robotics, yet learned hand skills remain limited to simple grasps or basic repeated motions, such as rotating an object. As robots attempt more demanding in-hand maneuvers, safety, such as not dropping the object, becomes critical, yet it is rarely measured. We study in-hand object pose reaching, a task that demands diverse and difficult grasps: moving an object to almost any position and orientation within reach of the fingers, at any wrist orientation, through long sequences of consecutive goals. We present a recipe for training such policies for a LEAP hand in simulation without human data, built on a range of insights, from diverse resets and goals via generated grasp datasets to an adaptive friction curriculum. With careful system identification, the policies transfer zero-shot to the real hand, using either motion capture or a single wrist-mounted depth camera, and prove remarkably capable and robust. On top of the task policy, we add a learned residual safety filter with adjustable conservativeness, letting us tune the balance between performance and safety and greatly reduce object drops. In 13.3 hours of real-robot evaluation over 4,800 unseen goals across 4 objects and diverse wrist orientations, the state-based policy reaches 95.2% of goals within 2 cm and 0.4 rad, and the filter cuts its drops by 75% while still reaching 91.5% of goals. The policies can also spin a lightbulb held in an arbitrary pose, reach out-of-distribution goals, withstand fast arm motion and perturbations, and, needing only a wrist camera, serve as portable skills such as screwing in a lightbulb. We release the full recipe as a platform for studying performance, reliability, and safety in high-dimensional dexterous systems. Videos and more are available at https://ian-chuang.github.io/dexsafe/.

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*自动采集于 2026-10-29*

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