WholeBodyVLA: 向统一的潜变量VLA迈进以实现全身体姿操控控制
WholeBodyVLA: Towards Unified Latent VLA for Whole-Body Loco-Manipulation Control
AI总结：
WholeBodyVLA通过统一的潜变量学习框架和专门设计的强化学习策略，实现了大空间人形运动-操控的精准控制与高效泛化。
摘要：
人形机器人需要精确的运动和灵巧的操控才能完成具有挑战性的运动-操控任务。然而，现有的方法，无论是模块化还是端到端的，都在操控意识的运动方面存在不足。这限制了机器人在一个有限的工作空间内，使其无法在大空间中进行运动-操控。我们将其归因于：（1）由于人形机器人远程操作数据稀缺，获取运动-操控知识具有挑战性；（2）执行运动命令的忠实和可靠执行困难，源于现有强化学习控制器精度和稳定性有限。为了获得更丰富的运动-操控知识，我们提出了一种统一的潜在学习框架，使视觉-语言-动作（VLA）系统能够从低成本的无动作注视视频中学习。此外，还设计了一种高效的人员数据收集管道来增强数据集并扩大收益。为了更精确地执行所需的运动命令，我们提出了一个面向运动-操控（LMO）的强化学习策略，专门针对准确且稳定的运动-操控动作，如前进、转弯和下蹲。基于这些组件，我们介绍了WholeBodyVLA，一种用于人形运动-操控的统一框架。据我们所知，WholeBodyVLA是唯一一种能够实现大空间人形运动-操控的框架。它通过在AgiBot X2人形机器人上的全面实验得到验证，比先前的基线方法提高了21.3%。它还展示了在广泛任务范围内的强大泛化能力和高可扩展性。
英文摘要：
Humanoid robots require precise locomotion and dexterous manipulation to perform challenging loco-manipulation tasks. Yet existing approaches, modular or end-to-end, are deficient in manipulation-aware locomotion. This confines the robot to a limited workspace, preventing it from performing large-space loco-manipulation. We attribute this to: (1) the challenge of acquiring loco-manipulation knowledge due to the scarcity of humanoid teleoperation data, and (2) the difficulty of faithfully and reliably executing locomotion commands, stemming from the limited precision and stability of existing RL controllers. To acquire richer loco-manipulation knowledge, we propose a unified latent learning framework that enables Vision-Language-Action (VLA) system to learn from low-cost action-free egocentric videos. Moreover, an efficient human data collection pipeline is devised to augment the dataset and scale the benefits. To execute the desired locomotion commands more precisely, we present a loco-manipulation-oriented (LMO) RL policy specifically tailored for accurate and stable core loco-manipulation movements, such as advancing, turning, and squatting. Building on these components, we introduce WholeBodyVLA, a unified framework for humanoid loco-manipulation. To the best of our knowledge, WholeBodyVLA is one of its kind enabling large-space humanoid loco-manipulation. It is verified via comprehensive experiments on the AgiBot X2 humanoid, outperforming prior baseline by 21.3%. It also demonstrates strong generalization and high extensibility across a broad range of tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
