LVDiffusor: Distilling Functional Rearrangement Priors from Large Models into Diffusor (2312.01474v2)

Abstract: Object rearrangement, a fundamental challenge in robotics, demands versatile strategies to handle diverse objects, configurations, and functional needs. To achieve this, the AI robot needs to learn functional rearrangement priors in order to specify precise goals that meet the functional requirements. Previous methods typically learn such priors from either laborious human annotations or manually designed heuristics, which limits scalability and generalization. In this work, we propose a novel approach that leverages large models to distill functional rearrangement priors. Specifically, our approach collects diverse arrangement examples using both LLMs and VLMs and then distills the examples into a diffusion model. During test time, the learned diffusion model is conditioned on the initial configuration and guides the positioning of objects to meet functional requirements. In this manner, we create a handshaking point that combines the strengths of conditional generative models and large models. Extensive experiments on multiple domains, including real-world scenarios, demonstrate the effectiveness of our approach in generating compatible goals for object rearrangement tasks, significantly outperforming baseline methods.

Introduction to Functional Rearrangement Priors

The capacity for a robot to rearrange objects in a way that meets specific functional needs is essential for various practical applications. This means a robot must understand how objects should be positioned to serve particular purposes, such as setting up a table for a left-handed person or organizing a workspace. Prior methods for teaching robots these functional arrangements have relied heavily on datasets marked by humans or on inflexible heuristic rules, neither of which easily scales or adapts to new environments.

Innovations in Data Collection

An innovative solution offered by this paper is the use of large pre-trained LLMs and visual LLMs (VLMs) to automatically generate diverse examples of object arrangements. These examples are then used to inform the training of a smaller, more nimble diffusion model. This process bypasses the previous limitations of scalability and generalizability. Specifically, the approach first uses VLMs to create initial examples, which are then refined with the aid of LLMs to ensure the generated arrangements align with the functional requirements specified by the prompt.

Training the Diffusion Model

The distillation into the conditional generative model, in this case, a diffusion model, is a key step. After accumulating a relevant dataset through the combination of LLMs and VLMs, this model learns to deduce and generate target poses for objects that satisfy the given functional requirements. The model incorporates time-dependent score functions, leveraging a continuous diffusion process that takes into account both the perturbed data distribution and the condition of the objects.

Real-World Application and Results

The real-world applicability of this approach is confirmed through experiments in various scenarios. These experiments demonstrate that the infused diffusion model can create arrangement plans that are in line with initial object configurations, outperforming baseline methods. Moreover, ablation studies confirm that both LLM and VLM contributions are crucial for efficient data distillation and functional rearrangement priors acquisition.

In summary, this innovative approach showcases a promising path forward for teaching robots to rearrange objects in functional, meaningful ways without extensive manual dataset preparation or heuristic design. This opens up new doors for robot deployments in dynamic and varied real-world settings.