Multistep Intent Estimation Guided Adaptive Passive Control for Safety-Aware 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 r...

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Vydáno v:IEEE transactions on cybernetics Ročník PP; s. 1 - 13
Hlavní autoři: Liu, Haotian, Zhang, Zhengtao, Tong, Yuchuang, Ju, Zhaojie
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 10.10.2025
Témata:
Accuracy
Behavioral sciences
Collaboration
Estimation
Force
Impedance
Model predictive control
multistep human intent estimation
passivity constraint
pHRC
reinforcement learning (RL)
Robot sensing systems
Robots
Stability analysis
Transformers
ISSN:2168-2267, 2168-2275, 2168-2275
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Shrnutí: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 physical human-robot collaboration (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.
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2025.3616392