用人体运动控制模型让机器人更自然地与人协作
Planning Human-Robot Co-manipulation with Human Motor Control Objectives and Multi-component Reaching Strategies
- 基于速度-精度权衡的生理模型优化机器人动作
- 在多种任务中生成类人轨迹，表现稳定可靠
- 适合需要精准人机协同的场景如康复或装配
为实现高效的人机目标导向协作，机器人需适应合作人类的意图与行为。现有方法中，解剖学模型仅能处理低层功能（如人体工效），数据驱动模型则泛化能力差或数据效率低。本文引入成熟的人体运动控制模型，基于速度-精度权衡与成本收益权衡，将人类轨迹建模为最小化目标函数的形式，并将其用于数值轨迹优化。该框架可扩展约束与新变量，实现协同运动规划与目标估计。我们在不确定目标达成和同步运动任务中部署该模型及多成分运动策略，在物理实验中验证了其在多种条件下生成类人轨迹的能力。
原文摘要 · Abstract (English)
For successful goal-directed human-robot interaction, the robot should adapt to the intentions and actions of the collaborating human. This can be supported by musculoskeletal or data-driven human models, where the former are limited to lower-level functioning such as ergonomics, and the latter have limited generalizability or data efficiency. What is missing, is the inclusion of human motor control models that can provide generalizable human behavior estimates and integrate into robot planning methods. We use well-studied models from human motor control based on the speed-accuracy and cost-benefit trade-offs to plan collaborative robot motions. In these models, the human trajectory minimizes an objective function, a formulation we adapt to numerical trajectory optimization. This can then be extended with constraints and new variables to realize collaborative motion planning and goal estimation. We deploy this model, as well as a multi-component movement strategy, in physical collaboration with uncertain goal-reaching and synchronized motion tasks, showing the ability of the approach to produce human-like trajectories over a range of conditions.
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