Visual Manipulation with Legs
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_ | 1866913561351028736 |
|---|---|
| author | He, Xialin Yuan, Chengjing Zhou, Wenxuan Yang, Ruihan Held, David Wang, Xiaolong |
| author_facet | He, Xialin Yuan, Chengjing Zhou, Wenxuan Yang, Ruihan Held, David Wang, Xiaolong |
| contents | Animals use limbs for both locomotion and manipulation. We aim to equip quadruped robots with similar versatility. This work introduces a system that enables quadruped robots to interact with objects using their legs, inspired by non-prehensile manipulation. The system has two main components: a visual manipulation policy module and a loco-manipulator module. The visual manipulation policy, trained with reinforcement learning (RL) using point cloud observations and object-centric actions, decides how the leg should interact with the object. The loco-manipulator controller manages leg movements and body pose adjustments, based on impedance control and Model Predictive Control (MPC). Besides manipulating objects with a single leg, the system can select from the left or right leg based on critic maps and move objects to distant goals through base adjustment. Experiments evaluate the system on object pose alignment tasks in both simulation and the real world, demonstrating more versatile object manipulation skills with legs than previous work. Videos can be found at https://legged-manipulation.github.io/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_11345 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Visual Manipulation with Legs He, Xialin Yuan, Chengjing Zhou, Wenxuan Yang, Ruihan Held, David Wang, Xiaolong Robotics Animals use limbs for both locomotion and manipulation. We aim to equip quadruped robots with similar versatility. This work introduces a system that enables quadruped robots to interact with objects using their legs, inspired by non-prehensile manipulation. The system has two main components: a visual manipulation policy module and a loco-manipulator module. The visual manipulation policy, trained with reinforcement learning (RL) using point cloud observations and object-centric actions, decides how the leg should interact with the object. The loco-manipulator controller manages leg movements and body pose adjustments, based on impedance control and Model Predictive Control (MPC). Besides manipulating objects with a single leg, the system can select from the left or right leg based on critic maps and move objects to distant goals through base adjustment. Experiments evaluate the system on object pose alignment tasks in both simulation and the real world, demonstrating more versatile object manipulation skills with legs than previous work. Videos can be found at https://legged-manipulation.github.io/ |
| title | Visual Manipulation with Legs |
| topic | Robotics |
| url | https://arxiv.org/abs/2410.11345 |