Hamza Merzic, Miroslav Bogdanovic, Daniel Kappler, Ludovic Righetti and Jeannette Bohg

Abstract. Grasping objects under uncertainty remains an open problem in robotics research. This uncertainty is often due to noisy or partial observations of the object pose or shape. To enable a robot to react appropriately to unforeseen effects, it is crucial that it continuously takes sensor feedback into account. While visual feedback is important for inferring a grasp pose and reaching for an object, contact feedback offers valuable information during manipulation and grasp acquisition. In this paper, we use model-free deep reinforcement learning to synthesize control policies that exploit contact sensing to generate robust grasping under uncertainty. We demonstrate our approach on a multi-fingered hand that exhibits more complex finger coordination than the commonly used two-fingered grippers. We conduct extensive experiments in order to assess the performance of the learned policies, with and without contact sensing. While it is possible to learn grasping policies without contact sensing, our results suggest that contact feedback allows for a significant improvement of grasping robustness under object pose uncertainty and for objects with a complex shape.

Pre-Print on arXiv

Code on GitHub using the free and open-source Gazebo simulator

Bibtex

@inproceedings{2019_ICRA_mbkrb,

  author    = {Hamza Merzic and

               Miroslav Bogdanovic and

               Daniel Kappler and

               Ludovic Righetti and

               Jeannette Bohg},

  title     = {Leveraging Contact Forces for Learning to Grasp},

  booktitle = {2019 IEEE International Conference on Robotics and Automation},

  year      = {2019},

  url       = {http://arxiv.org/abs/1809.07004}

}

Research done by

Machines in Motion Laboratory

Interactive Perception and Robot Learning lab

Autonomous Motion Department