Synergies Between Affordance and Geometry: 6-DoF Grasp Detection via Implicit Representations

TL;DR

This paper introduces a multi-task learning approach using implicit neural representations to improve 6-DoF grasp detection in cluttered scenes, achieving state-of-the-art success rates in simulation and real-world tests.

Contribution

It presents a novel method combining grasp affordance and 3D reconstruction through shared implicit representations, enhancing grasp detection performance.

Findings

Over 10% improvement in grasp success rate

Effective joint learning of geometry and grasping tasks

State-of-the-art results in clutter removal scenarios

Abstract

Grasp detection in clutter requires the robot to reason about the 3D scene from incomplete and noisy perception. In this work, we draw insight that 3D reconstruction and grasp learning are two intimately connected tasks, both of which require a fine-grained understanding of local geometry details. We thus propose to utilize the synergies between grasp affordance and 3D reconstruction through multi-task learning of a shared representation. Our model takes advantage of deep implicit functions, a continuous and memory-efficient representation, to enable differentiable training of both tasks. We train the model on self-supervised grasp trials data in simulation. Evaluation is conducted on a clutter removal task, where the robot clears cluttered objects by grasping them one at a time. The experimental results in simulation and on the real robot have demonstrated that the use of implicit neural representations and joint learning of grasp affordance and 3D reconstruction have led to state-of-the-art grasping results. Our method outperforms baselines by over 10% in terms of grasp success rate. Additional results and videos can be found at https://sites.google.com/view/rpl-giga2021