Fast Perception for Human-Robot Handovers with Legged Manipulators
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Author / Producer
Date
2024-03
Publication Type
Conference Paper
ETH Bibliography
yes
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Abstract
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.
Permanent link
Publication status
published
External links
Editor
Book title
HRI '24: Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction
Journal / series
Volume
Pages / Article No.
734 - 742
Publisher
Association for Computing Machinery
Event
19th Annual ACM/IEEE International Conference on Human Robot Interaction (HRI 2024)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
legged robotics; physical human-robot interactions; Human-Robot Handover
Organisational unit
09570 - Hutter, Marco / Hutter, Marco
Notes
Funding
188596 - Perceptive Dynamic Locomotion on Rough Terrain (SNF)