Non-rigid Relative Placement through 3D Dense Diffusion

TL;DR

This paper introduces a novel vision-based method using dense diffusion to predict the placement of deformable objects relative to each other, enabling better generalization in complex, real-world manipulation tasks.

Contribution

It extends relative placement to deformable objects with a new concept called cross-displacement and demonstrates its effectiveness through dense diffusion learning.

Findings

Generalizes to unseen objects and scene configurations

Outperforms prior methods on deformable tasks

Works in both simulation and real-world settings

Abstract

The task of "relative placement" is to predict the placement of one object in relation to another, e.g. placing a mug onto a mug rack. Through explicit object-centric geometric reasoning, recent methods for relative placement have made tremendous progress towards data-efficient learning for robot manipulation while generalizing to unseen task variations. However, they have yet to represent deformable transformations, despite the ubiquity of non-rigid bodies in real world settings. As a first step towards bridging this gap, we propose ``cross-displacement" - an extension of the principles of relative placement to geometric relationships between deformable objects - and present a novel vision-based method to learn cross-displacement through dense diffusion. To this end, we demonstrate our method's ability to generalize to unseen object instances, out-of-distribution scene configurations, and multimodal goals on multiple highly deformable tasks (both in simulation and in the real world) beyond the scope of prior works. Supplementary information and videos can be found at https://sites.google.com/view/tax3d-corl-2024 .