Efficient In-Context Imitation Learning for Robotics using Keypoint Action Tokens and Large Language Models

Core Concepts

Large text-pretrained Transformers can effectively act as efficient in-context imitation learning machines for robotics, without the need for any additional training on robotics data.

Abstract

The paper introduces Keypoint Action Tokens (KAT), a framework that enables in-context imitation learning of human demonstrations by repurposing large Transformers pretrained on text as general sequence-to-sequence learners. Key highlights: KAT transforms visual observations into sequences of 3D keypoint tokens and action trajectories into sequences of end-effector pose tokens, allowing text-pretrained Transformers to learn imitation behaviors. Experiments show that KAT can achieve state-of-the-art performance in few-shot imitation learning (≤10-20 demonstrations) on a variety of everyday manipulation tasks, outperforming current imitation learning methods. KAT does not require any additional training on robotics data, leveraging the emergent pattern completion abilities of large language models pretrained on text. The authors analyze the optimal design choices for the keypoint and action token representations, as well as the performance of different generations of large language models as imitation learning machines. The results suggest that the rapid progress in large language models can directly benefit robotics, without the need for innovations in robotics-specific algorithms or data collection.

Stats

"We show that off-the-shelf text-based Transformers, with no additional training, can perform few-shot in-context visual imitation learning, mapping visual observations to action sequences that emulate the demonstrator's behaviour." "We show that these Transformers excel at translating tokenised visual keypoint observations into action trajectories, performing on par or better than state-of-the-art imitation learning (diffusion policies) in the low-data regime on a suite of real-world, everyday tasks."

Quotes

"A key contribution of our work is strong evidence that the progressive evolution of capabilities of large pretrained Transformers is leading to the emergence of more and more general and efficient pattern learning machines, that can directly be used off-the-shelf to tackle sequence-to-sequence imitation learning without the need for training on any robotics data." "The ability to repurpose large networks trained on language domains, where data is abundant, is a promising avenue to unlock unprecedented learning efficiency in robotics, where data is scarce."

Key Insights Distilled From

Keypoint Action Tokens Enable In-Context Imitation Learning in Robotics

by Norman Di Pa... at arxiv.org 03-29-2024

https://arxiv.org/pdf/2403.19578.pdf

Deeper Inquiries

How can the in-context learning capabilities of large language models be further improved to scale to larger datasets of demonstrations without performance degradation?

To enhance the in-context learning capabilities of large language models for scaling to larger datasets of demonstrations, several strategies can be implemented: Efficient Attention Mechanisms: Developing more efficient attention mechanisms within the Transformers can help reduce computational complexity and improve scalability. Techniques like sparse attention or adaptive attention can be explored to focus on relevant parts of the input sequences. Hierarchical Representations: Introducing hierarchical representations can help the model capture long-range dependencies more effectively. By organizing information hierarchically, the model can learn to generalize better across different scales of input data. Incremental Learning: Implementing techniques for incremental learning can allow the model to adapt to new demonstrations without forgetting previously learned patterns. Continual learning methods can help retain knowledge from past demonstrations while incorporating new information. Multi-Modal Fusion: Integrating multi-modal information, such as combining visual and textual inputs, can enhance the model's understanding of the environment. By fusing different modalities effectively, the model can learn more robust representations for in-context learning. Regularization Techniques: Applying regularization techniques like dropout, weight decay, or knowledge distillation can prevent overfitting and improve the model's generalization capabilities, especially when scaling to larger datasets.

How can the keypoint and action token representations be made more adaptive and dynamic to handle a wider range of visual observations and task requirements?

To make the keypoint and action token representations more adaptive and dynamic for handling diverse visual observations and task requirements, the following approaches can be considered: Dynamic Keyframe Selection: Implementing a mechanism to dynamically select keyframes based on the relevance and importance of visual features can improve the adaptability of keypoint representations. Adaptive keyframe selection can focus on salient regions of the input images. Attention Mechanisms: Integrating attention mechanisms within the keypoint extraction process can allow the model to focus on different parts of the input image based on the context. Attention can help prioritize key visual information for better representation. Temporal Information Encoding: Incorporating temporal information into the keypoint representation process can capture motion dynamics and temporal dependencies in the visual data. This can be achieved through recurrent or temporal convolutional layers. Task-Specific Tokenization: Customizing the tokenization process based on specific task requirements can make the action token representations more task-adaptive. Task-specific tokenization schemes can capture the nuances of different actions more effectively. Feedback Mechanisms: Implementing feedback mechanisms that adjust the token representations based on the model's performance can enable dynamic adaptation during inference. Feedback loops can refine the token representations iteratively.

What other robotics tasks beyond imitation learning could benefit from repurposing large language models trained on text data?

Large language models trained on text data can benefit a wide range of robotics tasks beyond imitation learning, including: Path Planning: Language models can assist in generating natural language instructions for robot path planning, enabling intuitive communication of complex trajectories and navigation tasks. Human-Robot Interaction: Language models can enhance human-robot interaction by enabling robots to understand and generate natural language responses, facilitating seamless communication in various scenarios. Task Description and Understanding: Large language models can aid in interpreting and generating textual task descriptions, allowing robots to understand high-level task requirements and execute them effectively. Environment Perception: Language models can assist in processing and interpreting textual descriptions of the environment, helping robots to navigate and interact with their surroundings more intelligently. Knowledge Transfer: By leveraging the knowledge encoded in text data, robots can benefit from pre-trained language models to transfer generalizable skills and adapt to new tasks efficiently.