Virtual elasto-plastic robot compliance to active environments
Humans exhibit a particular compliant behavior in interactions with their environment. Facilitated by fast physical reasoning, humans are able to rapidly alter their compliance, enhancing robustness and safety in active environments. Transferring these capabilities to robotics is of utmost importance particularly as major space agencies begin investigating the potential of cooperative robotic teams in space. In this scenario, robots in orbit or on planetary surfaces are meant to support astronauts in exploration, maintenance, and habitat building to reduce costs and risks of space missions. A major challenge for interactive robot teams is establishing the capability to act in and interact with dynamic environments. Analogous to humans, the robot should be not only particularly compliant in case of unexpected collisions with other systems but also able to cooperatively handle objects requiring accurate pose estimation and fast trajectory planning. Here, we show that these challenges can be attenuated through an enhancement of active robot compliance introducing a virtual plastic first-order impedance component. We present how elasto-plastic compliance can be realized via energy-based detection of active environments and how evasive motions can be enabled through adaptive plastic compliance. Two space teleoperation experiments using different robotic assets confirm the potential of the method to enhance robustness in interaction with articulated objects and facilitate robot cooperation. An experiment in a health care facility presents how the same method analogously solidifies robotic interactions in human-robot shared environments by giving the robot a subordinate role.
Authors
- Neal Y. Lii (ORCID: https://orcid.org/0000-0002-0801-0823)
- Werner Friedl (ORCID: https://orcid.org/0000-0003-3002-7274)
- Annette Hagengruber (ORCID: https://orcid.org/0000-0002-8243-1327)
- Jörn Vogel (ORCID: https://orcid.org/0000-0002-1987-0028)
- Robert J. Burger
- Xuwei Wu (ORCID: https://orcid.org/0000-0003-2616-9149)
- Peter Schmaus (ORCID: https://orcid.org/0000-0002-6639-0967)
- Gabriel Quere (ORCID: https://orcid.org/0000-0002-1788-3685)
- Maged Iskandar (ORCID: https://orcid.org/0000-0003-0644-0659)
- Michael Panzirsch (ORCID: https://orcid.org/0000-0002-0647-7147)
- Rute Luz (ORCID: https://orcid.org/0000-0001-5278-4204)
- Harsimran Singh (ORCID: https://orcid.org/0000-0002-6735-9945)
- J. Butterfaß
- Xiaozhou Luo (ORCID: https://orcid.org/0000-0002-4315-366X)
- Thomas Gumpert
- Alin Albu‐Schäffer (ORCID: https://orcid.org/0000-0001-5343-9074)
- Emiel den Exter
- Adrian S. Bauer (ORCID: https://orcid.org/0000-0002-1171-4709)
- Florian Lay (ORCID: https://orcid.org/0000-0002-5706-3278)
- Daniel Leidner (ORCID: https://orcid.org/0000-0001-5091-7122)
- Nesrine Batti (ORCID: https://orcid.org/0009-0002-2404-158X)
- Frederick H. Schmidt (ORCID: https://orcid.org/0000-0002-5620-1368)
- Thomas Krüeger
- Ajithkumar N. Manaparampil
- Luisa Mayershofer (ORCID: https://orcid.org/0009-0003-2587-6973)
- Pedro Pavelski
- Anne Koepken
- Samuel Bustamante-Gomez
Institutions
- University of Bremen (DE)
- Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR) (DE)
- Institut Polytechnique de Paris (FR)
- European Space Research and Technology Centre (NL)
- Technical University of Munich (DE)
Publication Details
- Journal
- Science Robotics
- Published
- 2025-02-26
- DOI
- https://doi.org/10.1126/scirobotics.adq1703
- Citations
- 6
- Primary Topic
- Robot Manipulation and Learning
- Type
- article
- Field-Weighted Citation Impact
- 7.29