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Multistep Intent Estimation Guided Adaptive Passive Control for Safety-Aware Physical Human-Robot Collaboration.
IEEE Transactions on Cybernetics
|October 10, 2025
Summary
This study introduces a new adaptive control framework for physical human-robot collaboration (pHRC). It enhances safety and efficiency by accurately predicting human intent and adjusting robot behavior in real-time.
Area of Science:
- Robotics
- Artificial Intelligence
- Control Systems
Background:
- Physical human-robot collaboration (pHRC) demands high safety and efficiency.
- Accurate human intent estimation and stable adaptive control are critical challenges in pHRC.
Purpose of the Study:
- To develop a novel two-loop adaptive passive control framework for safety-aware efficient pHRC.
- To improve human-robot disagreement reduction and robot assistance levels.
Main Methods:
- A two-loop framework with outer loop human intent estimation and inner loop adaptive passive control.
- Outer loop: Transformer-based Human Intent Estimator (THIE) with Conditional Variational Autoencoder (CVAE) for multistep predictions.
- Inner loop: Goal-Oriented Reinforcement Learning (GoRL)-based adaptive impedance control and Energy Tank-based Passive Model Predictive Control (ET-PMPC).
Main Results:
- The proposed framework significantly improves intent estimation accuracy and robot assistance levels.
- Experimental results demonstrate superior performance compared to state-of-the-art methods.
- The framework effectively balances disagreement and assistance while ensuring safety.
Conclusions:
- The novel framework advances safety-aware efficient physical human-robot collaboration.
- It offers a promising approach for real-time robot behavior adjustment based on human intent.
- The system enhances overall performance in human-robot interaction scenarios.
