PlayFusion: Skill Acquisition via Diffusion from Language-Annotated Play
This addresses the challenge of efficiently acquiring robotic skills from easily collected but suboptimal data, representing an incremental improvement by applying diffusion models to a known bottleneck in robotics.
The paper tackles the problem of learning goal-directed skill policies from unstructured, language-annotated play data in robotics, leveraging diffusion models to handle its multimodal and noisy nature, resulting in diverse robot behaviors demonstrated across simulation and real-world environments.
Learning from unstructured and uncurated data has become the dominant paradigm for generative approaches in language and vision. Such unstructured and unguided behavior data, commonly known as play, is also easier to collect in robotics but much more difficult to learn from due to its inherently multimodal, noisy, and suboptimal nature. In this paper, we study this problem of learning goal-directed skill policies from unstructured play data which is labeled with language in hindsight. Specifically, we leverage advances in diffusion models to learn a multi-task diffusion model to extract robotic skills from play data. Using a conditional denoising diffusion process in the space of states and actions, we can gracefully handle the complexity and multimodality of play data and generate diverse and interesting robot behaviors. To make diffusion models more useful for skill learning, we encourage robotic agents to acquire a vocabulary of skills by introducing discrete bottlenecks into the conditional behavior generation process. In our experiments, we demonstrate the effectiveness of our approach across a wide variety of environments in both simulation and the real world. Results visualizations and videos at https://play-fusion.github.io