Fast Perception for Human-Robot Handovers with Legged Manipulators

Deploying perception modules for human-robot handovers is challenging because they require a high degree of reactivity, generalizability, and robustness to work reliably for a diversity of cases. Further complications arise as each object can be handed over in a variety of ways, causing occlusions and viewpoint changes. On legged robots, deployment is particularly challenging because of the limited computational resources and the image-space noise resulting from locomotion. In this paper, we introduce an efficient and object-agnostic real-time tracking framework, specifically designed for human-to-robot handover tasks with a legged manipulator. The proposed method combines optical flow with Siamese-network-based tracking and depth segmentation in an adaptive Kalman Filter framework. We show that we outperform the state-of-the-art for tracking during human-to-robot handovers with our legged manipulator. We demonstrate the generalizability, reactivity, and robustness of our system through experiments in different scenarios and by carrying out a user study. Additionally, as timing is proven to be more important than spatial accuracy for human-robot handovers, we show that we reach close to human timing performance during the approaching phase, both in terms of objective metrics and subjective feedback from the participants of our user study.