Non-Gaited Legged Locomotion with Monte-Carlo Tree Search and Supervised Learning
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866915627542773760 |
|---|---|
| author | Taouil, Ilyass Amatucci, Lorenzo Khadiv, Majid Dai, Angela Barasuol, Victor Turrisi, Giulio Semini, Claudio |
| author_facet | Taouil, Ilyass Amatucci, Lorenzo Khadiv, Majid Dai, Angela Barasuol, Victor Turrisi, Giulio Semini, Claudio |
| contents | Legged robots are able to navigate complex terrains by continuously interacting with the environment through careful selection of contact sequences and timings. However, the combinatorial nature behind contact planning hinders the applicability of such optimization problems on hardware. In this work, we present a novel approach that optimizes gait sequences and respective timings for legged robots in the context of optimization-based controllers through the use of sampling-based methods and supervised learning techniques. We propose to bootstrap the search by learning an optimal value function in order to speed-up the gait planning procedure making it applicable in real-time. To validate our proposed method, we showcase its performance both in simulation and on hardware using a 22 kg electric quadruped robot. The method is assessed on different terrains, under external perturbations, and in comparison to a standard control approach where the gait sequence is fixed a priori. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_07508 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Non-Gaited Legged Locomotion with Monte-Carlo Tree Search and Supervised Learning Taouil, Ilyass Amatucci, Lorenzo Khadiv, Majid Dai, Angela Barasuol, Victor Turrisi, Giulio Semini, Claudio Robotics Legged robots are able to navigate complex terrains by continuously interacting with the environment through careful selection of contact sequences and timings. However, the combinatorial nature behind contact planning hinders the applicability of such optimization problems on hardware. In this work, we present a novel approach that optimizes gait sequences and respective timings for legged robots in the context of optimization-based controllers through the use of sampling-based methods and supervised learning techniques. We propose to bootstrap the search by learning an optimal value function in order to speed-up the gait planning procedure making it applicable in real-time. To validate our proposed method, we showcase its performance both in simulation and on hardware using a 22 kg electric quadruped robot. The method is assessed on different terrains, under external perturbations, and in comparison to a standard control approach where the gait sequence is fixed a priori. |
| title | Non-Gaited Legged Locomotion with Monte-Carlo Tree Search and Supervised Learning |
| topic | Robotics |
| url | https://arxiv.org/abs/2408.07508 |