Retrieval-Augmented Embodied Agents
CVPR 2024(2024)
摘要
Embodied agents operating in complex and uncertain environments face
considerable challenges. While some advanced agents handle complex manipulation
tasks with proficiency, their success often hinges on extensive training data
to develop their capabilities. In contrast, humans typically rely on recalling
past experiences and analogous situations to solve new problems. Aiming to
emulate this human approach in robotics, we introduce the Retrieval-Augmented
Embodied Agent (RAEA). This innovative system equips robots with a form of
shared memory, significantly enhancing their performance. Our approach
integrates a policy retriever, allowing robots to access relevant strategies
from an external policy memory bank based on multi-modal inputs. Additionally,
a policy generator is employed to assimilate these strategies into the learning
process, enabling robots to formulate effective responses to tasks. Extensive
testing of RAEA in both simulated and real-world scenarios demonstrates its
superior performance over traditional methods, representing a major leap
forward in robotic technology.
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