让双人机器人像人类一样互动,实现真实物理协作。
It Takes Two: Learning Interactive Whole-Body Control Between Humanoid Robots
- 通过接触感知动作重定向,保持双机器人协调一致
- 引入交互驱动控制器,提升接触真实性和动作连贯性
- 适合研究多机器人协作与人机交互的学者
类人机器人真正的潜力不在于单体自主,而在于两个或更多机器人之间进行物理上真实的、具有社会意义的全身互动,模仿人类社交行为的丰富性。然而,现有单机器人方法存在孤立问题,忽略代理间动态,导致接触错位、穿插和动作不自然。为此,我们提出 Harmanoid 框架,可将人类交互动作迁移至双机器人系统,同时保持运动学保真度与物理真实性。该框架包含两个核心组件:(i) 接触感知动作重定向,通过对齐SMPL接触点与机器人顶点恢复身体协调;(ii) 交互驱动动作控制器,利用特定交互奖励强化关键点协同与物理合理接触。通过显式建模代理间接触与交互感知动态,Harmanoid 抓住了单机器人框架所忽视的耦合行为。实验表明,Harmanoid 显著优于现有单机器人方法,在交互动作模仿任务中表现更优。
原文摘要 · Abstract (English)
The true promise of humanoid robotics lies beyond single-agent autonomy: two or more humanoids must engage in physically grounded, socially meaningful whole-body interactions that echo the richness of human social interaction. However, single-humanoid methods suffer from the isolation issue, ignoring inter-agent dynamics and causing misaligned contacts, interpenetrations, and unrealistic motions. To address this, we present Harmanoid , a dual-humanoid motion imitation framework that transfers interacting human motions to two robots while preserving both kinematic fidelity and physical realism. Harmanoid comprises two key components: (i) contact-aware motion retargeting, which restores inter-body coordination by aligning SMPL contacts with robot vertices, and (ii) interaction-driven motion controller, which leverages interaction-specific rewards to enforce coordinated keypoints and physically plausible contacts. By explicitly modeling inter-agent contacts and interaction-aware dynamics, Harmanoid captures the coupled behaviors between humanoids that single-humanoid frameworks inherently overlook. Experiments demonstrate that Harmanoid significantly improves interactive motion imitation, surpassing existing single-humanoid frameworks that largely fail in such scenarios.
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