Behavior Generation with Latent Actions
(2403.03181)Abstract
Generative modeling of complex behaviors from labeled datasets has been a longstanding problem in decision making. Unlike language or image generation, decision making requires modeling actions - continuous-valued vectors that are multimodal in their distribution, potentially drawn from uncurated sources, where generation errors can compound in sequential prediction. A recent class of models called Behavior Transformers (BeT) addresses this by discretizing actions using k-means clustering to capture different modes. However, k-means struggles to scale for high-dimensional action spaces or long sequences, and lacks gradient information, and thus BeT suffers in modeling long-range actions. In this work, we present Vector-Quantized Behavior Transformer (VQ-BeT), a versatile model for behavior generation that handles multimodal action prediction, conditional generation, and partial observations. VQ-BeT augments BeT by tokenizing continuous actions with a hierarchical vector quantization module. Across seven environments including simulated manipulation, autonomous driving, and robotics, VQ-BeT improves on state-of-the-art models such as BeT and Diffusion Policies. Importantly, we demonstrate VQ-BeT's improved ability to capture behavior modes while accelerating inference speed 5x over Diffusion Policies. Videos and code can be found https://sjlee.cc/vq-bet
Overview
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Vector-Quantized Behavior Transformers (VQ-BeT) is a novel AI approach for generating complex action sequences, showing superiority in modeling behaviors across different environments.
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VQ-BeT utilizes hierarchical vector quantization to discretize continuous actions, enabling efficient and nuanced modeling of action sequences with a transformer-based architecture.
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The method improves upon traditional behavior cloning and generative modeling by accurately capturing the multimodal nature of real-world decision-making in dynamic environments.
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Practical applications of VQ-BeT include sophisticated models for robotics and autonomous systems, with future prospects in enhancing generative modeling and decision-making in AI.
Enhancing Behavior Generation through Hierarchical Vector Quantization
Introduction to Vector-Quantized Behavior Transformers
Within the landscape of behavior modeling in artificial intelligence, generating complex, multimodal actions sequences reflective of real-world decision-making stands as a formidable challenge. Where traditional methods of behavior cloning or generative modeling may stumble in capturing the intricacies and variability inherent to dynamic environments, the novel approach of Vector-Quantized Behavior Transformers (VQ-BeT) emerges as a promising solution. VQ-BeT leverages the power of hierarchical vector quantization to tokenize continuous action spaces, subsequently enabling a transformer-based architecture to model and generate nuanced action sequences. This method has demonstrated superior performance across a range of environments including simulated manipulation, autonomous driving, and real-world robotics, setting new benchmarks in the field.
Technical Overview and Methodological Contributions
The core innovation of VQ-BeT lies in its use of a hierarchical vector quantization module to discretize continuous actions, a technique inspired by advancements in generative modeling of audio and visual media. This hierarchical approach allows for the efficient capturing of multimodal action distributions, addressing the limitations of previous k-means clustering methods used in Behavior Transformers (BeT).
VQ-BeT's architecture can be divided into two primary stages:
- Action Discretization Phase: Continuous actions are encoded into a latent space using a hierarchical vector quantization process, which efficiently compresses the action information into discrete tokens while preserving the action sequences' variability and richness.
- Behavior Generation Phase: The discretized actions serve as input to a transformer-based model, which, leveraging the temporal dependencies and multimodal nature of actions, generates action sequences conditioned on observed or partial environment states.
Across seven simulated environments, including tasks from simulated manipulation to autonomous driving, VQ-BeT has demonstrated not only improved accuracy in behavior prediction but also an enhanced ability to capture multiple modes of behavior, showcasing its robustness and versatility.
Implications and Future Prospects
The adoption of VQ-BeT for behavior generation carries several practical and theoretical implications:
- Improved Modeling of Complex Behaviors: By accurately capturing the multimodal nature of actions in diverse environments, VQ-BeT paves the way for more sophisticated models of decision-making that better reflect the variability seen in real-world behaviors.
- Enhanced Performance in Robotics and Autonomous Systems: The ability to generate nuanced, context-aware action sequences makes VQ-BeT particularly well-suited for applications in robotics and autonomous vehicles, where adaptability and decision-making under uncertainty are crucial.
- Future Developments in AI and Generative Modeling: The success of VQ-BeT suggests that further exploration of hierarchical vector quantization and transformer-based architectures could yield significant advances in other areas of AI, particularly in generative modeling tasks beyond behavior prediction.
In conclusion, VQ-BeT represents a significant step forward in the generative modeling of complex behaviors, offering a versatile and effective tool for capturing the dynamic, multimodal nature of real-world decision-making. As this research progresses, the potential applications and enhancements of VQ-BeT hint at an exciting future for artificial intelligence, robotics, and beyond.