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Grasp detection in clutter requires the robot to reason about the 3D scenefrom incomplete and noisy perception. In this work, we draw insight that 3Dreconstruction and grasp learning are two intimately connected tasks, both ofwhich require a fine-grained understanding of local geometry details. We thuspropose to utilize the synergies between grasp affordance and 3D reconstructionthrough multi-task learning of a shared representation. Our model takesadvantage of deep implicit functions, a continuous and memory-efficientrepresentation, to enable differentiable training of both tasks. We train themodel on self-supervised grasp trials data in simulation. Evaluation isconducted on a clutter removal task, where the robot clears cluttered objectsby grasping them one at a time. The experimental results in simulation and onthe real robot have demonstrated that the use of implicit neuralrepresentations and joint learning of grasp affordance and 3D reconstructionhave led to state-of-the-art grasping results. Our method outperforms baselinesby over 10% in terms of grasp success rate. Additional results and videos canbe found at https://sites.google.com/view/rpl-giga2021

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