外观
论文arXiv 2610.0812022 天前
iGPC框架：生成式运动先验驱动人形机器人物体交互策略部署
本文由 AI 根据公开内容整理，可能有误，请以原文为准：iGPC: Generative Motion Priors for Object-Aware Humanoid Interaction（arXiv 2610.08120）
核心内容
针对人形机器人在非结构化环境中结合全身控制与物体交互的难题，arXiv论文提出iGPC框架，将生成式预训练控制器（GPC）从通用人体运动扩展至全身人形-环境交互。该研究解决了将大规模人体运动先验有效迁移至感知驱动物体交互的瓶颈，实验证明该生成式先验能为接触丰富的真实世界任务提供可部署策略基础，涵盖抓取支撑物稳定身体及推移动态物体等复杂行为。
技术架构包含两阶段：首先基于场景功能线索与特权状态信息，利用配对支持场景的人体动作数据进行交互与功能预训练，构建掌握特定接触行为的交互专家；其次引入感知驱动的学生网络，保留预训练GPC策略并通过板载传感器蒸馏专家技能。训练目标不仅重构专家观测，还强制保留专家的运动决策与关节目标，从而弥合特权信息与感官输入间的差距，实现从仿真先验到实机感知的端到端迁移。
关键看点
- 交互专家微调：基于场景功能线索与特权状态适配GPC，利用配对场景数据预训练，习得抓握支撑、推动物体等特定接触行为
- 感知策略蒸馏：学生网络保留预训练GPC策略，通过板载传感蒸馏专家技能，训练目标兼顾观测重构与运动决策及关节目标保留
- 运动先验迁移：验证大规模生成式人体运动先验可作为人形机器人基础，有效支撑接触丰富真实环境中的可部署交互策略学习
工程与产业观察
iGPC证实了将通用人体运动生成模型作为具身交互底座的可行性，其“特权专家+感知学生”蒸馏范式为解决Sim-to-Real中接触动力学与视觉感知对齐提供了新路径。对于人形算法团队，该工作表明在缺乏海量机器人交互数据时，利用人类运动先验进行功能条件化微调是降低真机试错成本、提升泛化能力的关键工程手段。
英文摘要 (Abstract)
Humanoid robots operating in unstructured environments must combine robust whole-body control with the ability to perceive and physically interact with surrounding objects. While large-scale human motion data provides powerful priors for natural and versatile humanoid control, effectively transferring such priors to perception-driven object interaction remains challenging. To address this bottleneck, we propose a framework that extends the recently proposed Generative Pretrained Controller (GPC) from general human motion to full-body humanoid-environment interaction. First, we adapt GPC into interaction experts conditioned on scene affordance cues and privileged state information. This adaptation begins with interaction and affordance pre-training on human motions paired with support scenes. These experts leverage the pretrained human motion prior while learning task-specific contact behaviors, including reaching toward objects, grasping environmental supports for stabilization, and pushing movable objects. Second, we introduce a perception-driven student that retains the pretrained GPC policy and distills interaction skills from the experts using onboard sensory observations. To bridge the gap between privileged expert observations and sensory inputs, we train the student's perception not only to reconstruct expert observations, but also to preserve the expert's motion decisions and resulting joint targets. Notably, our experiments across multiple whole-body interaction tasks demonstrate that large-scale generative human motion priors provide an effective foundation for learning deployable policies for humanoid interactions in contact-rich real-world environments.
作者：Anujith Muraleedharan, Abdul Ahad Butt, Nolan Fey, Yash Prabhu, Anamika J H, Sandor Felber, Maurice Rahme, Ivan Laptev
来源
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