Edinburgh Research Explorer

Learning Pregrasp Manipulation of Objects from Ungraspable Poses

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

  • Zhaole Sun
  • Kai Yuan
  • Wenbin Hu
  • Chuanyu Yang
  • Zhibin Li

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Original languageEnglish
Title of host publication2020 International Conference on Robotics and Automation (ICRA)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages8
Publication statusAccepted/In press - 21 Jan 2020
Event2020 International Conference on Robotics and Automation - Virtual conference, France
Duration: 31 May 202031 Aug 2020


Conference2020 International Conference on Robotics and Automation
Abbreviated titleICRA 2020
CityVirtual conference
Internet address


In robotic grasping, objects are often occluded in ungraspable configurations such that no pregrasp pose can be found, eg large flat boxes on the table that can only be grasped from the side. Inspired by humans’ bimanual manipulation, eg one hand to lift up things and the other to grasp, we address this type of problems by introducing pregrasp manipulation – push and lift actions. We propose a model-free Deep Reinforcement Learning framework to train control policies that utilize visual information and proprioceptive states of the robot to autonomously discover robust pregrasp manipulation. The robot arm learns to first push the object towards a support surface and establishes a pivot to lift up one side of the object, thus creating a clearance between the object and the table for possible grasping solutions. Furthermore, we show the effectiveness of our proposed learning framework in training robust pregrasp policies that can directly transfer from simulation to real hardware through suitable design of training procedures, state, and action space. Lastly, we evaluate the effectiveness and the generalisation ability of the learned policies in real-world experiments, and demonstrate pregrasp manipulation of objects with various size, shape, weight, and surface friction.


2020 International Conference on Robotics and Automation


Virtual conference, France

Event: Conference

ID: 140941083