ActiveGrasp: Information-Guided Active Grasping with Calibrated Energy-based Model

TL;DR

This paper introduces ActiveGrasp, a novel approach combining a calibrated energy-based model for grasp distribution with active view selection to improve robotic grasping in cluttered environments, demonstrating superior performance over existing methods.

Contribution

We propose a calibrated energy-based model for grasp pose generation and an active view selection strategy based on information gain, addressing limitations of previous approaches.

Findings

Successfully grasp objects in cluttered environments with limited views

Outperforms previous state-of-the-art models in simulated and real setups

Provides a reproducible simulation platform for active grasping research

Abstract

Grasping in a densely cluttered environment is a challenging task for robots. Previous methods tried to solve this problem by actively gathering multiple views before grasp pose generation. However, they either overlooked the importance of the grasp distribution for information gain estimation or relied on the projection of the grasp distribution, which ignores the structure of grasp poses on the SE(3) manifold. To tackle these challenges, we propose a calibrated energy-based model for grasp pose generation and an active view selection method that estimates information gain from grasp distribution. Our energy-based model captures the multi-modality nature of grasp distribution on the SE(3) manifold. The energy level is calibrated to the success rate of grasps so that the predicted distribution aligns with the real distribution. The next best view is selected by estimating the information gain for grasp from the calibrated distribution conditioned on the reconstructed environment, which could efficiently drive the robot to explore affordable parts of the target object. Experiments on simulated environments and real robot setups demonstrate that our model could successfully grasp objects in a cluttered environment with limited view budgets compared to previous state-of-the-art models. Our simulated environment can serve as a reproducible platform for future research on active grasping. The source code of our paper will be made public when the paper is released to the public.