Human torque estimation improves exoskeleton assistance across tasks
General Performance Guarantee for Human Torque Estimation-Based Task-Agnostic Assistive Exoskeleton Control
RoboticsMachine Learning
Summary
Helping people move with robotic exoskeletons requires the machine to understand what the person wants to do. However, estimating human joint forces can be tricky and mistakes can make the robot less helpful. The authors define when the robot's help truly matches the person's movement and create a math-based way to ensure the robot's assistance is helpful more often. Their method works well even for new tasks the robot hasn't seen before. Testing on a real upper-arm exoskeleton showed the approach makes movements smoother and easier for people.
What this means in practice
- •For exoskeleton designers: Create assistive devices that reliably support users across many different tasks without retraining for each activity.
- •For rehabilitation engineers: Develop therapy systems that smoothly reduce patient effort during varied exercises with guaranteed helpful robot assistance.
Authors
Duy Hoang, Bastien Berret, Olivier Bruneau, Laurent Fribourg
Abstract
Accurate human torque estimation is crucial for enabling task-agnostic control in robotic exoskeleton systems. However, estimation errors may cause mismatches between the robot assistance and the human intention, degrading controllability and task performance. In this paper, we address this issue by formally defining matched assistance as scenarios in which the robot positively contributes to human movement. Based on this definition, we develop a theoretical framework to design the robot's desired interaction torque that guarantees a lower bound on the matched assistance probability. Importantly, the proposed guarantee holds over the entire torque distribution, including unseen data beyond the training tasks. This provides our method with strong reliability and generalization, both of which are critical for effective exoskeleton control. The proposed strategy is implemented on the ABLE upper-limb exoskeleton and evaluated in a multi-task setup. Experimental results validate the theoretical guarantees and demonstrate that the proposed strategy achieves effective general performance across several tasks, guaranteeing movement smoothness while reducing human physical effort.