Behavior Generation with Latent Actions
<|reference_start|>Behavior Generation with Latent Actions: 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<|reference_end|>
@article{lee2024behavior,
    title={Behavior Generation with Latent Actions},
    author={Seungjae Lee and Yibin Wang and Haritheja Etukuru and H. Jin Kim and
  Nur Muhammad Mahi Shafiullah and Lerrel Pinto},
    journal={arXiv preprint arXiv:2403.03181},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.03181},
    primaryClass={cs.LG cs.AI cs.RO}
}