Dynamic Locomotion on Slippery Ground
Open accessAbstract
Dynamic locomotion on unstructured and uneven terrain is a challenging task in legged robotics. Especially when it comes to slippery ground conditions, common state estimation and control algorithms suffer from the usual no-slip assumption. In fact, there has been only little research on this subject. This paper addresses the problem of slipping by treating slip detection and recovery tasks separately. Our contribution to the former is a probabilistic slip estimator based on aHidden Markov Model. In the second part of this paper, we propose impedance control and friction modulation as useful tools to recover stability during traction loss. We demonstrate the success of our estimation/control architecture by enabling ANYmal, a quadrupedal torque-controllable robot, to dynamically walk over slippery terrain.
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Conventional controllers for legged locomotion are based on elaborate state machines that explicitly trigger the execution of motion primitives and reflexes.
“Many rely on elaborate state machines that coordinate the execution of motion primitives and reflex controllers [1-5].”
From Learning quadrupedal locomotion over challenging terrain (2020) · cites this paper as [1]
To date, no published work has demonstrated dynamic locomotion in diverse, challenging natural environments as shown in Fig. 1.
“To date, no published work has demonstrated dynamic locomotion in diverse, challenging natural environments as shown in Fig. 1.”
From Learning quadrupedal locomotion over challenging terrain (2020) · cites this paper as [1]
Cites
ANYmal - a highly mobile and dynamic quadrupedal robot
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Self-citationForces Acting on a Biped Robot. Center of Pressure—Zero Moment Point
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Self-citationContact Model Fusion for Event-Based Locomotion in Unstructured Terrains
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Learning quadrupedal locomotion over challenging terrain
Lee, J., Hwangbo, J., Wellhausen, L., Koltun, V., & Hutter, M. (2020) · Science Robotics · doi:10.1126/scirobotics.abc5986 · Cited by 1,156 · see citing works
Self-citationHuman Motion Control of Quadrupedal Robots using Deep Reinforcement Learning
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- Authors
- Jenelten, F., Hwangbo, J., Tresoldi, F., Bellicoso, C. D., & Hutter, M.
- Affiliations
- ETH Zurich (CH)
- Venue
- IEEE Robotics and Automation Letters
- Published
- 2019-10
- Volume
- 4
- Issue
- 4
- Pages
- 4170-4176
- ISSN
- 2377-3766 (ISSN-L)
- 2377-3774
- OpenAlex
- W2963465704
- Cited by
- 84
- References
- 19
- Citations by year
- 2026: 6 · 2025: 21 · 2024: 13 · 2023: 13 · 2022: 13 · 2021: 9 · 2020: 8 · 2019: 1
- FWCI
- 2.74
- Type
- journal article
- Language
- en
- Open access
- green
- infoscience.epfl.ch
- Licence
- Locations
- IEEE Robotics and Automation Letters (publishedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, other-oa)
- ETH Zürich Research Collection
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Robotic Locomotion and Control
- Physical Sciences › Engineering › Control and Systems Engineering › Control and Dynamics of Mobile Robots
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
- Keywords
- dynamic locomotion, slippery terrain, legged robots, slip detection, slip estimation, hidden Markov models, impedance control, friction modulation, traction loss, ANYmal, quadrupedal robots, uneven terrain
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