Task-relevant grasp selection: A joint solution to planning grasps and manipulative motion trajectories

Amir Masoud Ghalamzan Esfahani, Nikos Mavrakis, Marek Kopicki, Rustam Stolkin, Ales Leonardis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Citations (Scopus)


This paper addresses the problem of jointly planning both grasps and subsequent manipulative actions. Previously, these two problems have typically been studied in isolation, however joint reasoning is essential to enable robots to complete real manipulative tasks. In this paper, the two problems are addressed jointly and a solution that takes both into consideration is proposed. To do so, a manipulation capability index is defined, which is a function of both the task execution waypoints and the object grasping contact points. We build on recent state-of-the-art grasp-learning methods, to show how this index can be combined with a likelihood function computed by a probabilistic model of grasp selection, enabling the planning of grasps which have a high likelihood of being stable, but which also maximise the robot's capability to deliver a desired post-grasp task trajectory. We also show how this paradigm can be extended, from a single arm and hand, to enable efficient grasping and manipulation with a bi-manual robot. We demonstrate the effectiveness of the approach using experiments on a simulated as well as a real robot.
Original languageEnglish
Title of host publication2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
PublisherIEEE Xplore
ISBN (Electronic)9781509037629
ISBN (Print)9781509037636
Publication statusPublished - 1 Dec 2016


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