Active vision for dexterous grasping of novel objects

By Ermano Arruda, Jeremy L. Wyatt, Marek Kopicki. 2016

45 citations recorded by the source corpus.

How should a robot direct active vision so as to ensure reliable grasping? We answer this question for the case of dexterous grasping of unfamiliar objects. By dexterous grasping we simply mean grasping by any hand with more than two fingers, such that the robot has some choice about where to place each finger. Such grasps typically fail in one of two ways, either unmodeled objects in the scene cause collisions or object reconstruction is insufficient to ensure that the grasp points provide a stable force closure. These problems can be solved more easily if active sensing is guided by the anticipated actions. Our approach has three stages. First, we take a single view and generate candidate grasps from the resulting partial object reconstruction. Second, we drive the active vision approach to maximise surface reconstruction quality around the planned contact points. During this phase, the anticipated grasp is continually refined. Third, we direct gaze to improve the safety of the planned reach to grasp trajectory. We show, on a dexterous manipulator with a camera on the wrist, that our approach (80.4% success rate) outperforms a randomised algorithm (64.3% success rate).

Subfields: Aerospace Engineering, Biomedical Engineering, Control and Systems Engineering

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Citations in the corpus

Learning a visuomotor controller for real world robotic grasping using simulated depth images (2017, background): “ature design or speciﬁcation. An active vision approach by Arruda et al. acquires sensor data from different view points to optimize surface reconstruction for reliable grasping during grasp planning [14]. However, the actual grasping does not use sensor feedback. Levine et al. were one of the ﬁrst ”
Learning robust, real-time, reactive robotic grasping (2019, methodology): “Prior work has proposed methods for overcoming this challenge by performing point cloud fusion from multiple viewpoints, and performing grasp pose detection on the fused point cloud (Arruda et al., 2016; Kahn et al., 2015; ten Pas et al., 2017).”