SayTap: Language to Quadrupedal Locomotion
Large language models (LLMs) have demonstrated the potential to perform
high-level planning. Yet, it remains a challenge for LLMs to comprehend
low-level commands, such as joint angle targets or motor torques. This paper
proposes an approach to use foot contact patterns as an interface that bridges
human commands in natural language and a locomotion controller that outputs
these low-level commands. This results in an interactive system for quadrupedal
robots that allows the users to craft diverse locomotion behaviors flexibly. We
contribute an LLM prompt design, a reward function, and a method to expose the
controller to the feasible distribution of contact patterns. The results are a
controller capable of achieving diverse locomotion patterns that can be
transferred to real robot hardware. Compared with other design choices, the
proposed approach enjoys more than 50% success rate in predicting the correct
contact patterns and can solve 10 more tasks out of a total of 30 tasks. Our
project site is: https://saytap.github.io.
64891faa7987267e4fc3f608
Yujin Tang
claimed_verified
2023-06-14T12:14:58.914Z
63285beda4102056bc198395
/avatars/6c936818a3814c1c32a8eaae8b8cb6d6.svg
yujin tang
user
tyj2022
64891faa7987267e4fc3f609
Wenhao Yu
64891faa7987267e4fc3f60a
Jie Tan
64891faa7987267e4fc3f60b
Heiga Zen
64891faa7987267e4fc3f60c
Aleksandra Faust
claimed_verified
2023-07-04T07:24:07.283Z
64a3b828225b2121f6b3beb3
https://cdn-avatars.huggingface.co/v1/production/uploads/64a3b828225b2121f6b3beb3/44AgAQENc7cJMOWcoK4g_.jpeg
Aleksandra Faust
user
sandraorion
64891faa7987267e4fc3f60d
Tatsuya Harada
SayTap: Language to Quadrupedal Locomotion
Large language models (LLMs) have demonstrated the potential to perform
high-level planning. Yet, it remains a challenge for LLMs to comprehend
low-level commands, such as joint angle targets or motor torques. This paper
proposes an approach to use foot contact patterns as an interface that bridges
human commands in natural language and a locomotion controller that outputs
these low-level commands. This results in an interactive system for quadrupedal
robots that allows the users to craft diverse locomotion behaviors flexibly. We
contribute an LLM prompt design, a reward function, and a method to expose the
controller to the feasible distribution of contact patterns. The results are a
controller capable of achieving diverse locomotion patterns that can be
transferred to real robot hardware. Compared with other design choices, the
proposed approach enjoys more than 50% success rate in predicting the correct
contact patterns and can solve 10 more tasks out of a total of 30 tasks. Our
project site is: https://saytap.github.io.
A novel approach using-foot contact patterns interfaces natural language commands with LLMs to control quadrupedal robots, achieving high success rates and versatility in locomotion tasks.
large language models (LLMs) foot contact patterns locomotion controller joint angle targets motor torques prompt design reward function feasible distribution of contact patterns quadrupedal robots locomotion behaviors