The paper introduces Retrieval-Augmented Embodied Agents (RAEA), a novel framework that aims to improve the capabilities of embodied agents operating in complex and uncertain environments. RAEA utilizes an external policy memory bank containing a diverse set of robotic experiences and scenarios, which it can access and leverage to enhance the agent's performance.
The key components of RAEA are:
Policy Retriever: This module is adept at handling multi-modal inputs, including instructions (text, audio) and observations (images, videos, point clouds). It can efficiently retrieve relevant policies from the external memory bank based on the current input.
Policy Generator: This module processes the retrieved policies and integrates the relevant information into the main policy network, enabling the agent to formulate effective responses to the current task.
The authors conduct extensive evaluations of RAEA on both simulated benchmarks (Franka Kitchen, Meta-World, Maniskill-2) and real-world datasets. The results demonstrate that RAEA significantly outperforms state-of-the-art methods, particularly in low-data scenarios, highlighting the effectiveness of the retrieval-augmentation approach.
The paper also presents several ablation studies to investigate the impact of various components, such as the use of multiple modalities, the inclusion of action and proprioceptive state information, and the diversity of the policy memory bank. These studies provide valuable insights into the key factors that contribute to the superior performance of RAEA.
Overall, the Retrieval-Augmented Embodied Agents framework represents a significant advancement in the field of robotics, offering a novel and practical approach to leveraging collective knowledge from diverse datasets to enhance the capabilities of embodied agents.
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by Yichen Zhu,Z... at arxiv.org 04-19-2024
https://arxiv.org/pdf/2404.11699.pdfDeeper Inquiries