Precise Pick-and-Place using Score-Based Diffusion Networks

TL;DR

This paper introduces a diffusion network-based method for precise, continuous pose estimation to improve robotic pick-and-place tasks, demonstrating high accuracy and robustness through extensive experiments.

Contribution

It presents a novel coarse-to-fine diffusion approach for continuous pose estimation, enhancing manipulation precision in robotic pick-and-place operations.

Findings

Improved success rates in simulated and real-world tasks

Effective pose estimation with limited data through augmentation

Validated high-precision manipulation via extensive experiments

Abstract

In this paper, we propose a novel coarse-to-fine continuous pose diffusion method to enhance the precision of pick-and-place operations within robotic manipulation tasks. Leveraging the capabilities of diffusion networks, we facilitate the accurate perception of object poses. This accurate perception enhances both pick-and-place success rates and overall manipulation precision. Our methodology utilizes a top-down RGB image projected from an RGB-D camera and adopts a coarse-to-fine architecture. This architecture enables efficient learning of coarse and fine models. A distinguishing feature of our approach is its focus on continuous pose estimation, which enables more precise object manipulation, particularly concerning rotational angles. In addition, we employ pose and color augmentation techniques to enable effective training with limited data. Through extensive experiments in simulated and real-world scenarios, as well as an ablation study, we comprehensively evaluate our proposed methodology. Taken together, the findings validate its effectiveness in achieving high-precision pick-and-place tasks.