StructDiffusion: Language-Guided Creation of Physically-Valid Structures using Unseen Objects
This addresses the challenge of enabling robots to perform semantic rearrangement tasks in human environments without step-by-step instructions, representing a strong incremental advance.
The paper tackles the problem of robots rearranging unseen objects into physically-valid structures based on high-level language goals, achieving an average 16% improvement in success rate over existing methods.
Robots operating in human environments must be able to rearrange objects into semantically-meaningful configurations, even if these objects are previously unseen. In this work, we focus on the problem of building physically-valid structures without step-by-step instructions. We propose StructDiffusion, which combines a diffusion model and an object-centric transformer to construct structures given partial-view point clouds and high-level language goals, such as "set the table". Our method can perform multiple challenging language-conditioned multi-step 3D planning tasks using one model. StructDiffusion even improves the success rate of assembling physically-valid structures out of unseen objects by on average 16% over an existing multi-modal transformer model trained on specific structures. We show experiments on held-out objects in both simulation and on real-world rearrangement tasks. Importantly, we show how integrating both a diffusion model and a collision-discriminator model allows for improved generalization over other methods when rearranging previously-unseen objects. For videos and additional results, see our website: https://structdiffusion.github.io/.