Precise Pick-and-Place using Score-Based Diffusion Networks
This work addresses precision challenges in robotic manipulation, particularly for tasks requiring accurate object pose estimation, though it appears incremental as it builds on existing diffusion network techniques.
The paper tackles the problem of improving precision in robotic pick-and-place operations by proposing a coarse-to-fine continuous pose diffusion method, resulting in enhanced success rates and manipulation accuracy as validated through experiments.
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.