Finding safe 3D robot grasps through efficient haptic exploration with unscented Bayesian optimization and collision penalty

Abstract

Robust grasping is a major, and still unsolved, problem in robotics. Information about the 3D shape of an object can be obtained either from prior knowledge (e.g., accurate models of known objects or approximate models of familiar objects) or real-time sensing (e.g., partial point clouds of unknown objects) and can be used to identify good potential grasps. However, due to modeling and sensing inaccuracies, local exploration is often needed to refine such grasps and successfully apply them in the real world. The recently proposed unscented Bayesian optimization technique can make such exploration safer by selecting grasps that are robust to uncertainty in the input space (e.g., inaccuracies in the grasp execution). Extending our previous work on 2D optimization, in this paper we propose a 3D haptic exploration strategy that combines unscented Bayesian optimization with a novel collision penalty heuristic to find safe grasps in a very efficient way: while by augmenting the search-space to 3D we are able to find better grasps, the collision penalty heuristic allows us to do so without increasing the number of exploration steps.

arXiv comment2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Cited by in corpus (5)

• Visuo-Haptic Object Perception for Robots: An Overview
• Simultaneous Tactile Exploration and Grasp Refinement for Unknown Objects
• Bayesian optimization for robust robotic grasping using a sensorized compliant hand
• Active Tapping via Gaussian Process for Efficient Unknown Object Surface Reconstruction
• Robust Policy Search for Robot Navigation