Learning Dynamic Robot-to-Human Object Handover from Human Feedback
This addresses the challenge of collaborative physical interaction in robotics for applications like elderly care and manufacturing, though it appears incremental as it builds on existing policy search methods.
The paper tackles the problem of enabling robots to perform dynamic object handover with humans, such as handing water bottles to moving runners, by learning from human feedback. The result is a robot that learns to hand over objects naturally and adapts to human motion dynamics, as shown in preliminary experiments.
Object handover is a basic, but essential capability for robots interacting with humans in many applications, e.g., caring for the elderly and assisting workers in manufacturing workshops. It appears deceptively simple, as humans perform object handover almost flawlessly. The success of humans, however, belies the complexity of object handover as collaborative physical interaction between two agents with limited communication. This paper presents a learning algorithm for dynamic object handover, for example, when a robot hands over water bottles to marathon runners passing by the water station. We formulate the problem as contextual policy search, in which the robot learns object handover by interacting with the human. A key challenge here is to learn the latent reward of the handover task under noisy human feedback. Preliminary experiments show that the robot learns to hand over a water bottle naturally and that it adapts to the dynamics of human motion. One challenge for the future is to combine the model-free learning algorithm with a model-based planning approach and enable the robot to adapt over human preferences and object characteristics, such as shape, weight, and surface texture.