Online Learning of Human Constraints from Feedback in Shared Autonomy
This work addresses the problem of enhancing human-robot collaboration in shared autonomy by reducing workload and discomfort for human operators, though it appears incremental as it builds on existing constraint-learning approaches.
The paper tackles the challenge of real-time collaboration between humans and assistive robots by learning a model of human physical constraints from feedback, enabling the robot to adapt its actions to align with the ergonomic preferences and limitations of the human operator.
Real-time collaboration with humans poses challenges due to the different behavior patterns of humans resulting from diverse physical constraints. Existing works typically focus on learning safety constraints for collaboration, or how to divide and distribute the subtasks between the participating agents to carry out the main task. In contrast, we propose to learn a human constraints model that, in addition, considers the diverse behaviors of different human operators. We consider a type of collaboration in a shared-autonomy fashion, where both a human operator and an assistive robot act simultaneously in the same task space that affects each other's actions. The task of the assistive agent is to augment the skill of humans to perform a shared task by supporting humans as much as possible, both in terms of reducing the workload and minimizing the discomfort for the human operator. Therefore, we propose an augmentative assistant agent capable of learning and adapting to human physical constraints, aligning its actions with the ergonomic preferences and limitations of the human operator.