Multistep Intent Estimation Guided Adaptive Passive Control for Safety-Aware Physical Human–Robot Collaboration
提出一个双环自适应被动控制框架，通过多步人类意图估计减少人机分歧并提升机器人辅助水平，实验证明在意图估计精度、辅助水平和安全性上优于现有方法。
physical human-robot collaboration (pHRC) requires strict safety and efficiency guarantees, imposing heightened demands on accurate human intent estimation and adaptive control in a stable manner. To address these challenges, we propose a novel two-loop adaptive passive control framework guided by multistep human intent estimation to reduce human-robot disagreement and improve robot assistance level, facilitating safety-aware efficient pHRC. In the framework, outer loop's intent estimation guides the inner loop's adaptive passive controller, ensuring real-time robot behavior adjustment based on multistep intention. Specifically, the outer loop incorporates a transformer-based human intent estimator (THIE) that integrates the Transformer with a conditional variational autoencoder (CVAE) for multistep predictions, accurately estimating motion and force to guide the robot. The inner loop incorporates a goal-oriented reinforcement learning (GoRL)-based adaptive impedance control, which constructs multistep rewards based on prediction and probability from THIE to adjust impedance parameters, thereby balancing disagreement and assistance, and promoting locally optimal robot behaviors. Furthermore, an energy tank-based passive model predictive control (ET-PMPC) is employed to limit robot stored energy, avoiding the impact of variable impedance on safety. Experiments validate that our framework outperforms state-of-the-art (SOTA) methods, significantly improving intent estimation accuracy, robot assistance level, and safety, highlighting its potential to advance pHRC.