Online Multi-Contact Receding Horizon Planning via Value Function Approximation Article Swipe

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Jiayi Wang , Sang-Hyun Kim , Teguh Santoso Lembono , Wenqian Du , Jae‐Hyun Shim , Samadi Saeid , Ke Wang , Vladimir Ivan , Sylvain Calinon , Sethu Vijayakumar , Steve Tonneau ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2306.04732

Planning multi-contact 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 non-convex dynamics of multi-contact motions, this approach is computationally expensive. To enable online Receding Horizon Planning (RHP) of multi-contact 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 receding horizon planning of our real-world humanoid robot Talos walking in dynamic environments that change on-the-fly.

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Artificial Intelligence

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Horizon Traverse Time horizon Humanoid robot Bellman equation Motion planning Computer science Computation Mathematical optimization Function (biology) Trajectory Oracle Robot Fidelity Artificial intelligence Mathematics Algorithm Evolutionary biology Telecommunications Geometry Geodesy Geography Software engineering Physics Biology Astronomy

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2306.04732
PDF: https://arxiv.org/pdf/2306.04732
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OpenAlex ID: https://openalex.org/W4380136084

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DOI: https://doi.org/10.48550/arxiv.2306.04732

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Title: Online Multi-Contact Receding Horizon Planning via Value Function Approximation

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Language: en

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Publication year: 2023

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Publication date: 2023-06-07

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Authors: Jiayi Wang, Sang-Hyun Kim, Teguh Santoso Lembono, Wenqian Du, Jae‐Hyun Shim, Samadi Saeid, Ke Wang, Vladimir Ivan, Sylvain Calinon, Sethu Vijayakumar, Steve Tonneau

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Landing page: https://arxiv.org/abs/2306.04732

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Concepts: Horizon, Traverse, Time horizon, Humanoid robot, Bellman equation, Motion planning, Computer science, Computation, Mathematical optimization, Function (biology), Trajectory, Oracle, Robot, Fidelity, Artificial intelligence, Mathematics, Algorithm, Evolutionary biology, Telecommunications, Geometry, Geodesy, Geography, Software engineering, Physics, Biology, Astronomy

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abstract_inverted_index.the	14, 19, 29, 45, 48, 53, 79, 92, 104, 109, 118, 173, 186
abstract_inverted_index.use	136
abstract_inverted_index.RHP,	122
abstract_inverted_index.RHP.	149
abstract_inverted_index.This	194
abstract_inverted_index.best	187
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abstract_inverted_index.find	75
abstract_inverted_index.show	181
abstract_inverted_index.that	43, 182, 215
abstract_inverted_index.this	59
abstract_inverted_index.with	16, 96, 112
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abstract_inverted_index.Model	100
abstract_inverted_index.Talos	210
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abstract_inverted_index.given	52
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abstract_inverted_index.value	10, 30, 80, 105, 143
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abstract_inverted_index.Levels	98
abstract_inverted_index.beyond	47
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abstract_inverted_index.enable	65
abstract_inverted_index.future	46
abstract_inverted_index.model.	116
abstract_inverted_index.namely	94, 120
abstract_inverted_index.online	66, 201
abstract_inverted_index.oracle	126
abstract_inverted_index.slopes	171
abstract_inverted_index.static	177
abstract_inverted_index.tasks,	133
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abstract_inverted_index.dynamic	213
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abstract_inverted_index.fashion	7
abstract_inverted_index.former,	93
abstract_inverted_index.future,	20
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abstract_inverted_index.horizon	6, 40, 111, 203
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abstract_inverted_index.relaxed	115
abstract_inverted_index.respect	17
abstract_inverted_index.results	180
abstract_inverted_index.slopes,	167
abstract_inverted_index.walking	164, 211
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abstract_inverted_index.Multiple	97
abstract_inverted_index.Planning	0, 69
abstract_inverted_index.Receding	67
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abstract_inverted_index.approach	60
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abstract_inverted_index.converge	192
abstract_inverted_index.dynamics	55
abstract_inverted_index.evaluate	151
abstract_inverted_index.foresees	44
abstract_inverted_index.function	11, 31, 106
abstract_inverted_index.horizon.	50
abstract_inverted_index.humanoid	162, 208
abstract_inverted_index.maintain	176
abstract_inverted_index.moderate	166
abstract_inverted_index.momentum	23
abstract_inverted_index.motions,	58, 73
abstract_inverted_index.online).	193
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abstract_inverted_index.receding	5, 202
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abstract_inverted_index.traverse	25
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abstract_inverted_index.approach.	90
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abstract_inverted_index.centroidal	158
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abstract_inverted_index.locomotion	132
abstract_inverted_index.non-convex	54
abstract_inverted_index.objectives	130, 139
abstract_inverted_index.obstacles.	27
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abstract_inverted_index.approximated	33
abstract_inverted_index.environments	214
abstract_inverted_index.trajectories	36, 159
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abstract_inverted_index.multi-contact	1, 57, 72
abstract_inverted_index.short-horizon	148
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abstract_inverted_index.approximations	77
abstract_inverted_index.learning-based	89
abstract_inverted_index.locally-guided	183
abstract_inverted_index.computationally	62
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countries_distinct_count	0
institutions_distinct_count	11
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citation_normalized_percentile