Metric-Based Imitation Learning Between Two Dissimilar Anthropomorphic Robotic Arms
This addresses the challenge of enabling robots with different embodiments to learn from human demonstrations, though it is incremental as it builds on existing imitation learning methods.
The paper tackled the correspondence problem in imitation learning between two dissimilar anthropomorphic robotic arms by introducing a distance measure, which was used as a loss function for static pose imitation and as feedback in a model-free deep reinforcement learning framework for dynamic movement imitation in simulation, finding it well-suited for describing similarity and learning policies.
The development of autonomous robotic systems that can learn from human demonstrations to imitate a desired behavior - rather than being manually programmed - has huge technological potential. One major challenge in imitation learning is the correspondence problem: how to establish corresponding states and actions between expert and learner, when the embodiments of the agents are different (morphology, dynamics, degrees of freedom, etc.). Many existing approaches in imitation learning circumvent the correspondence problem, for example, kinesthetic teaching or teleoperation, which are performed on the robot. In this work we explicitly address the correspondence problem by introducing a distance measure between dissimilar embodiments. This measure is then used as a loss function for static pose imitation and as a feedback signal within a model-free deep reinforcement learning framework for dynamic movement imitation between two anthropomorphic robotic arms in simulation. We find that the measure is well suited for describing the similarity between embodiments and for learning imitation policies by distance minimization.