Online Multicontact Receding Horizon Planning via Value Function Approximation Article Swipe
Jiayi Wang
,
Sang-Hyun Kim
,
Teguh Santoso Lembono
,
Wenqian Du
,
Jaehyun Shim
,
Saeid Samadi
,
Ke Wang
,
Vladimir Ivan
,
Sylvain Calinon
,
Sethu Vijayakumar
,
Steve Tonneau
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/tro.2024.3392154
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/tro.2024.3392154
Planning multicontact motions in a receding horizon fashion requires a value function to guide the planning with respect to the future, e.g., building momentum to traverse large obstacles. Traditionally, the value function is approximated by computing trajectories in a prediction horizon (never executed) that foresees the future beyond the execution horizon. However, given the nonconvex dynamics of multicontact motions, this approach is computationally expensive. To enable online receding horizon planning (RHP) of multicontact motions, we find efficient approximations of the value function. Specifically, we propose a trajectory-based and a learning-based approach. In the former, namely RHP with multiple levels of model fidelity, we approximate the value function by computing the prediction horizon with a convex relaxed model. In the latter, namely locally guided RHP, we learn an oracle to predict local objectives for locomotion tasks, and we use these local objectives to construct local value functions for guiding a short-horizon RHP. We evaluate both approaches in simulation by planning centroidal trajectories of a humanoid robot walking on moderate slopes, and on large slopes where the robot cannot maintain static balance. Our results show that locally guided RHP achieves the best computation efficiency (95%-98.6% cycles converge online). This computation advantage enables us to demonstrate online RHP of our real-world humanoid robot Talos walking in dynamic environments that change on-the-fly.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tro.2024.3392154
- OA Status
- green
- Cited By
- 9
- References
- 71
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4394994326
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4394994326Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/tro.2024.3392154Digital Object Identifier
- Title
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Online Multicontact Receding Horizon Planning via Value Function ApproximationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-01-01Full publication date if available
- Authors
-
Jiayi Wang, Sang-Hyun Kim, Teguh Santoso Lembono, Wenqian Du, Jaehyun Shim, Saeid Samadi, Ke Wang, Vladimir Ivan, Sylvain Calinon, Sethu Vijayakumar, Steve TonneauList of authors in order
- Landing page
-
https://doi.org/10.1109/tro.2024.3392154Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://infoscience.epfl.ch/record/311383Direct OA link when available
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Function (biology), Value (mathematics), Computer science, Control theory (sociology), Artificial intelligence, Mathematics, Machine learning, Evolutionary biology, Biology, Control (management)Top concepts (fields/topics) attached by OpenAlex
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9Total citation count in OpenAlex
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2025: 6, 2024: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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