Autoregressive Action Sequence Learning for Robotic Manipulation (2410.03132v5)

Abstract: Designing a universal policy architecture that performs well across diverse robots and task configurations remains a key challenge. In this work, we address this by representing robot actions as sequential data and generating actions through autoregressive sequence modeling. Existing autoregressive architectures generate end-effector waypoints sequentially as word tokens in LLMing, which are limited to low-frequency control tasks. Unlike language, robot actions are heterogeneous and often include continuous values -- such as joint positions, 2D pixel coordinates, and end-effector poses -- which are not easily suited for language-based modeling. Based on this insight, we introduce a straightforward enhancement: we extend causal transformers' single-token prediction to support predicting a variable number of tokens in a single step through our Chunking Causal Transformer (CCT). This enhancement enables robust performance across diverse tasks of various control frequencies, greater efficiency by having fewer autoregression steps, and lead to a hybrid action sequence design by mixing different types of actions and using a different chunk size for each action type. Based on CCT, we propose the Autoregressive Policy (ARP) architecture, which solves manipulation tasks by generating hybrid action sequences. We evaluate ARP across diverse robotic manipulation environments, including Push-T, ALOHA, and RLBench, and show that ARP, as a universal architecture, matches or outperforms the environment-specific state-of-the-art in all tested benchmarks, while being more efficient in computation and parameter sizes. Videos of our real robot demonstrations, all source code and the pretrained models of ARP can be found at http://github.com/mlzxy/arp.

• The paper introduces the ARP model that uses the Chunking Causal Transformer for multi-token predictions in robotic tasks.
• It employs an attention interleaving strategy with teacher-forcing to optimize autoregressive learning in robotic manipulation.
• Experimental results on environments like Push-T, ALOHA, and RLBench show superior performance and efficiency over current methods.

The paper "Autoregressive Action Sequence Learning for Robotic Manipulation" explores the application of autoregressive models, which have been highly successful in natural language processing, to robotic manipulation tasks. The authors introduce a novel architecture called the Chunking Causal Transformer (CCT), which adapts the traditional next-token prediction capability of causal transformers to allow multi-token prediction in a single pass. This adaptation aims to enhance efficiency and performance in generating action sequences for robots.

The CCT leverages an innovative attention interleaving strategy that optimizes the training process through a method known as teacher-forcing. This technique aids the model in learning desirable action sequences by feeding the correct output token sequences back into the model during training. The authors then use CCT to develop the Autoregressive Policy (ARP) model, which is specifically designed to learn and generate action sequences in an autoregressive manner.

The key advantage of ARP is its ability to capture and exploit the underlying causal relationships inherent in robotic tasks, which is achieved through the autoregressive formulation. Consequently, this approach facilitates more accurate and efficient action sequence prediction and planning.

The effectiveness of the ARP model is empirically demonstrated across various robotic manipulation environments, including Push-T, ALOHA, and RLBench. In these settings, the ARP model outperforms existing state-of-the-art methods, demonstrating its superiority in computational efficiency and reduced parameter size. This represents a significant step forward in the use of machine learning models for robotic manipulation, where efficiency and performance are crucial.

The paper provides additional resources such as video demonstrations, source code, and pre-trained models, available on the author's GitHub repository, for researchers and practitioners interested in exploring and applying these methods in their own robotic systems.