Adaptive Compliance Policy: Learning Approximate Compliance for Diffusion Guided Control
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_ | 1866929745539629056 |
|---|---|
| author | Hou, Yifan Liu, Zeyi Chi, Cheng Cousineau, Eric Kuppuswamy, Naveen Feng, Siyuan Burchfiel, Benjamin Song, Shuran |
| author_facet | Hou, Yifan Liu, Zeyi Chi, Cheng Cousineau, Eric Kuppuswamy, Naveen Feng, Siyuan Burchfiel, Benjamin Song, Shuran |
| contents | Compliance plays a crucial role in manipulation, as it balances between the concurrent control of position and force under uncertainties. Yet compliance is often overlooked by today's visuomotor policies that solely focus on position control. This paper introduces Adaptive Compliance Policy (ACP), a novel framework that learns to dynamically adjust system compliance both spatially and temporally for given manipulation tasks from human demonstrations, improving upon previous approaches that rely on pre-selected compliance parameters or assume uniform constant stiffness. However, computing full compliance parameters from human demonstrations is an ill-defined problem. Instead, we estimate an approximate compliance profile with two useful properties: avoiding large contact forces and encouraging accurate tracking. Our approach enables robots to handle complex contact-rich manipulation tasks and achieves over 50\% performance improvement compared to state-of-the-art visuomotor policy methods. For result videos, see https://adaptive-compliance.github.io/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_09309 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Adaptive Compliance Policy: Learning Approximate Compliance for Diffusion Guided Control Hou, Yifan Liu, Zeyi Chi, Cheng Cousineau, Eric Kuppuswamy, Naveen Feng, Siyuan Burchfiel, Benjamin Song, Shuran Robotics Compliance plays a crucial role in manipulation, as it balances between the concurrent control of position and force under uncertainties. Yet compliance is often overlooked by today's visuomotor policies that solely focus on position control. This paper introduces Adaptive Compliance Policy (ACP), a novel framework that learns to dynamically adjust system compliance both spatially and temporally for given manipulation tasks from human demonstrations, improving upon previous approaches that rely on pre-selected compliance parameters or assume uniform constant stiffness. However, computing full compliance parameters from human demonstrations is an ill-defined problem. Instead, we estimate an approximate compliance profile with two useful properties: avoiding large contact forces and encouraging accurate tracking. Our approach enables robots to handle complex contact-rich manipulation tasks and achieves over 50\% performance improvement compared to state-of-the-art visuomotor policy methods. For result videos, see https://adaptive-compliance.github.io/ |
| title | Adaptive Compliance Policy: Learning Approximate Compliance for Diffusion Guided Control |
| topic | Robotics |
| url | https://arxiv.org/abs/2410.09309 |