In-Context Learning Enables Robot Action Prediction in LLMs (2410.12782v2)

Abstract: Recently, LLMs have achieved remarkable success using in-context learning (ICL) in the language domain. However, leveraging the ICL capabilities within LLMs to directly predict robot actions remains largely unexplored. In this paper, we introduce RoboPrompt, a framework that enables off-the-shelf text-only LLMs to directly predict robot actions through ICL without training. Our approach first heuristically identifies keyframes that capture important moments from an episode. Next, we extract end-effector actions from these keyframes as well as the estimated initial object poses, and both are converted into textual descriptions. Finally, we construct a structured template to form ICL demonstrations from these textual descriptions and a task instruction. This enables an LLM to directly predict robot actions at test time. Through extensive experiments and analysis, RoboPrompt shows stronger performance over zero-shot and ICL baselines in simulated and real-world settings. Our project page is available at https://davidyyd.github.io/roboprompt.

• The paper introduces RoboPrompt, a framework that uses in-context learning to predict robot actions from textual descriptions without additional fine-tuning.
• It transforms robot action episodes into structured text through keyframe identification and prompt construction, achieving a 51.8% success rate across 16 RLBench tasks.
• Real-world experiments with a Franka Emika Panda robot highlight RoboPrompt’s robustness to pose estimation errors and its potential for versatile autonomous manipulation.

In-Context Learning Enables Robot Action Prediction in LLMs

In the paper titled "In-Context Learning Enables Robot Action Prediction in LLMs," the authors present a novel framework, RoboPrompt, which leverages the capabilities of LLMs to predict robot actions without requiring additional training. This approach capitalizes on the in-context learning (ICL) abilities intrinsic to LLMs, a feature that has remained largely unexplored in the domain of robotics.

Key Contributions

The principal contribution of this work is the introduction of a method that empowers text-only LLMs to perform robot action prediction directly through ICL. Notably, RoboPrompt achieves this by transforming episodes of robot actions into textual descriptions, allowing the LLM to process them as structured ICL demonstrations. This transformation is achieved without any finetuning of the models, distinguishing RoboPrompt from traditional approaches requiring extensive training data.

Methodology

The RoboPrompt framework comprises three critical steps:

1. Keyframe Identification: The method identifies keyframes from robot action episodes based on joint velocities and gripper state changes. This reduction effectively condenses the episode while preserving essential information for action prediction.
2. Textual Representation: Essential elements of an episode, including the end-effector actions and estimated object poses, are converted into textual descriptions. This transformation ensures compatibility with the processing capabilities of LLMs.
3. ICL Prompt Construction: A structured prompt is formulated from the textual descriptions and task instructions to produce ICL examples. During inference, these examples enable the LLM to predict novel robot actions based on new observations.

Results and Analysis

The empirical evaluation of RoboPrompt spans both simulated environments and real-world settings, demonstrating superior performance over several zero-shot and ICL baselines. Specifically, RoboPrompt achieved a 51.8% average success rate across 16 RLBench tasks, a notable improvement over other methods like VoxPoser and KAT. This performance is attributed to the efficacy of ICL in leveraging structured prompts without further model training.

In real-world experiments using a Franka Emika Panda robot, RoboPrompt maintained high success rates in manipulation tasks, highlighting its applicability to practical scenarios. The paper also reveals its robustness to pose estimation errors and its scaling capability with the number of ICL examples, further validating its potential utility.

Implications and Future Directions

The implications of RoboPrompt are significant for the field of robotics, offering a pathway to deploy LLMs for robot instruction and manipulation tasks without the overhead of extensive retraining. The framework's reliance on textual data aligns well with the typical input LLMs are designed to handle, enabling seamless integration into existing robotic workflows.

Looking forward, the paper identifies several areas for exploration. The adaptation of RoboPrompt to high-frequency control tasks, such as those required by humanoid robots, represents a potential avenue for enhancement. Additionally, extending the framework to more complex, multi-agent, or bimanual manipulation scenarios could broaden its applicability.

While the results are promising, the reliance on static observations (open-loop planning) invites opportunities for improving dynamic task adaptability (closed-loop approaches). Future work may include incorporating continuous feedback mechanisms to refine predictions iteratively.

Conclusion

The RoboPrompt framework illustrates the compelling potential of LLMs for robotics, offering a methodology that bridges language processing and robotic action prediction through ICL. Its success underscores the emergent utility of LLMs beyond traditional NLP settings, promising enhanced capabilities for autonomous systems across various domains.