Large Language Models for Orchestrating Bimanual Robots
arxiv(2024)
摘要
Although there has been rapid progress in endowing robots with the ability to
solve complex manipulation tasks, generating control policies for bimanual
robots to solve tasks involving two hands is still challenging because of the
difficulties in effective temporal and spatial coordination. With emergent
abilities in terms of step-by-step reasoning and in-context learning, Large
Language Models (LLMs) have taken control of a variety of robotic tasks.
However, the nature of language communication via a single sequence of discrete
symbols makes LLM-based coordination in continuous space a particular challenge
for bimanual tasks. To tackle this challenge for the first time by an LLM, we
present LAnguage-model-based Bimanual ORchestration (LABOR), an agent utilizing
an LLM to analyze task configurations and devise coordination control policies
for addressing long-horizon bimanual tasks. In the simulated environment, the
LABOR agent is evaluated through several everyday tasks on the NICOL humanoid
robot. Reported success rates indicate that overall coordination efficiency is
close to optimal performance, while the analysis of failure causes, classified
into spatial and temporal coordination and skill selection, shows that these
vary over tasks. The project website can be found at
http://labor-agent.github.io
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