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Shelving, Stacking, Hanging: Relational Pose Diffusion for Multi-modal Rearrangement (2307.04751v1)

Published 10 Jul 2023 in cs.RO, cs.CV, and cs.LG

Abstract: We propose a system for rearranging objects in a scene to achieve a desired object-scene placing relationship, such as a book inserted in an open slot of a bookshelf. The pipeline generalizes to novel geometries, poses, and layouts of both scenes and objects, and is trained from demonstrations to operate directly on 3D point clouds. Our system overcomes challenges associated with the existence of many geometrically-similar rearrangement solutions for a given scene. By leveraging an iterative pose de-noising training procedure, we can fit multi-modal demonstration data and produce multi-modal outputs while remaining precise and accurate. We also show the advantages of conditioning on relevant local geometric features while ignoring irrelevant global structure that harms both generalization and precision. We demonstrate our approach on three distinct rearrangement tasks that require handling multi-modality and generalization over object shape and pose in both simulation and the real world. Project website, code, and videos: https://anthonysimeonov.github.io/rpdiff-multi-modal/

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Relational Pose Diffusion for Multi-modal Object Rearrangement

The paper "Shelving, Stacking, Hanging: Relational Pose Diffusion for Multi-modal Rearrangement" introduces a novel system for performing rearrangement tasks with unknown objects by estimating their desired object-scene relationships using point clouds. The authors focus specifically on addressing the complexities involved in dealing with novel geometries, poses, and layouts, leveraging a diffusion model approach to predict 6-DoF transformations that accommodate these new configurations.

At the core of the approach, termed Relational Pose Diffusion (ReDiff), is the application of diffusion models to iteratively refine predicted poses of objects to achieve desired spatial relations with a target scene. The system's input consists of 3D point clouds captured by depth cameras, representing the object to be rearranged and the scene. Key to the system's performance is its iterative pose de-noising mechanism, which allows the method to handle multi-modality in predicted transformations — a significant obstacle in object rearrangement tasks where multiple solutions can satisfy the desired arrangement.

Notably, ReDiff demonstrates its efficacy across different tasks such as placing books on shelves, hanging mugs on racks, and stacking cans within a cabinet, both in simulation and in real-world scenarios. The system is designed to generalize across various shapes and poses while maintaining precision, accomplished by selectively focusing on relevant local geometries and reducing the influence of irrelevant global structures.

The system's primary strength lies in its multi-modal pose prediction capability, facilitated by iterative de-noising. Instead of outputting a single best guess, ReDiff produces a set of diverse potential rearrangement outputs, increasing the likelihood of finding a viable solution that satisfies additional deployment constraints, like workspace limits. The iterative nature of ReDiff, akin to diffusion models' stepwise refinement in generative tasks, allows it to navigate through various plausible configurations progressively, honing in on a rearrangement solution tailored to the specific scene.

The numerical evaluation reflects strong performance in the tasks assessed, with ReDiff outperforming various baseline models that either struggle with the inherent multi-modal nature of complex scenes or fail to maintain precision. For example, while coarser classification-based approaches like Coarse-to-Fine Q-attention (C2F-QA) provided competitive results in less multi-modal tasks, they lacked the precise rotation and translation outputs achievable by ReDiff's refined iterative procedure.

Moreover, the framework is notable for its scalability to unseen environments, a critical factor when considering practical real-world deployment. This is facilitated by the method's robust architectural and training decisions, such as the use of local scene cropping during processing, encouraging generalization by focusing the model's learning on locally relevant features while ignoring far-field distractions.

The paper sets the stage for further investigation into enhancing similar systems' adaptability and precision in dynamic, high-variability environments. Future advancements might explore integrating additional sensory data for enhanced interaction understanding or scaling the method to accommodate articulated or deformable objects.

In conclusion, the paper outlines a significant step forward in multi-modal object rearrangement for robotics, offering a scalable and precise approach through the elegant utilization of iterative refinement inherent in diffusion models. This framework not only advances current methodologies but also opens avenues for more intricate and adaptable robotic manipulation systems in unstructured environments.

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Authors (8)
  1. Anthony Simeonov (14 papers)
  2. Ankit Goyal (21 papers)
  3. Lucas Manuelli (10 papers)
  4. Lin Yen-Chen (12 papers)
  5. Alina Sarmiento (1 paper)
  6. Alberto Rodriguez (79 papers)
  7. Pulkit Agrawal (103 papers)
  8. Dieter Fox (201 papers)
Citations (34)
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