Towards Seamless Handovers with Legged Manipulators (Talk)

Deploying perception and control modules for handovers is challenging because they require a high degree of robustness and generalizability to work reliably for a diversity of objects and situations, but also adaptivity to adjust to individual preferences. On legged robots, deployment is particularly challenging because of the limited computational resources and the additional sensing noise resulting from locomotion. In this talk, I will discuss how we tackle some of these challenges, by first introducing our perception framework and discussing the insights of the first human-robot handover user study with legged manipulators. Furthermore, I will show how we combine imitation and reinforcement learning to achieve some degree of adaptivity during handovers. Finally, I will present our work in which the robot takes into account the post-handover task of the collaboration partner when handing over an object. This is beneficial for situations where the human range of motion is constrained during the handover or time is crucial.