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Retrieval-Augmented Embodied Agents (2404.11699v1)

Published 17 Apr 2024 in cs.RO

Abstract: 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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Citations (8)
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Summary

  • The paper presents RAEA, which enhances embodied agent performance by integrating policy retrieval with dynamic action synthesis.
  • It leverages multi-modal encoders and transformers to retrieve and generate policies from a large-scale repository, enabling superior benchmark results.
  • Extensive evaluations demonstrate that RAEA outperforms state-of-the-art methods, especially in low-data scenarios and real-world tasks.

Retrieval-Augmented Embodied Agents: An Overview

The paper "Retrieval-Augmented Embodied Agents" introduces an innovative framework designed to enhance the capabilities of embodied agents operating in complex and uncertain environments. This work leverages a retrieval mechanism that accesses a repository of policies, enabling robots to recall strategies akin to how humans utilize past experiences to solve new challenges.

Methodology

The proposed system, termed Retrieval-Augmented Embodied Agent (RAEA), integrates two primary components: a policy retriever and a policy generator. The policy retriever accesses a policy memory bank that contains large-scale robotic data across multiple embodiments and modalities. The retriever identifies policies relevant to the current input, which can consist of instructions in text or audio formats and observations in image, video, or point cloud formats. The policy generator then synthesizes these retrieved policies to predict actions suitable for the current task.

The framework is built upon advances in multi-modal encoders and transformers. Specifically, it leverages ImageBind for processing various input types and implements a multi-modal policy retriever to handle the diverse modalities. This setup ensures that the retrieval and generation processes are robust and capable of generalizing across different scenarios and tasks.

Experimental Evaluation

The efficacy of RAEA is demonstrated through extensive evaluations on multiple benchmarks including Franka Kitchen, Meta-World, and ManiSkill-2, as well as real-world environments. The results strongly indicate that RAEA outperforms existing state-of-the-art methods, particularly in low-data regimes. For instance, in the Franka Kitchen and Meta-World benchmarks, RAEA achieved higher success rates compared to baseline methods such as R3M and BLIP-2, especially when trained with only ten demonstrations.

Further experiments on ManiSkill-2, which involve rigid body and soft body tasks, reinforced RAEA's superiority. The model was tested with both image-only and image-plus-point-cloud observations, exhibiting significant performance gains over baseline techniques.

Real-world experiments also showcased RAEA’s versatility and effectiveness. The authors collected a diverse dataset comprising 40 tasks with various objects and instructions. RAEA's incorporation of multi-modal inputs and status information (proprioception and action data) resulted in improved generalizability and success rates.

Implications and Future Directions

The introduction of RAEA provides a compelling framework for enhancing embodied agents by equipping them with external memory capabilities akin to human recall. This not only broadens the scope of tasks these agents can perform but also improves their adaptability in dynamic and uncertain environments.

From a practical standpoint, RAEA's ability to utilize diverse data sources and modalities makes it highly applicable in real-world settings where tasks and environments are seldom homogenous. The underlying architecture, built on advanced multi-modal encoders and transformers, ensures scalability and robustness.

Theoretically, this approach bridges the gap between human cognitive processes and robotic learning, offering insights into how external memory can be leveraged in artificial systems. Future research could explore optimizing the retrieval mechanism further, perhaps incorporating more advanced neural retrieval models or adaptive learning mechanisms that update the policy memory bank in real-time.

Moreover, expanding the repository to include more varied embodiments and tasks could further enhance generalization. Integration with large multi-modal models might provide additional avenues for real-time correction and learning, making embodied agents even more versatile and capable.

Conclusion

The paper presents a well-grounded and thoroughly evaluated framework that significantly advances the capabilities of embodied agents. By leveraging a retrieval-augmented approach, the authors bridge the gap between human-like learning and robotic applications. The implications of this work are vast, promising improvements in both practical deployments and theoretical advancements in AI and robotics.

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