Simulation of the underactuated Sake Robotics Gripper in V-REP

The Sake Robotics Gripper is a cheap, robust and versatile underactuated gripper that has not been simulated yet. The simulated model has to be able to interpret the same ROS messages the real gripper receives. This paper proposes a reproduction of the Sake Robotics Gripper in V-REP. We analyze the tools provided by V-REP to develop an algorithm for simulating the underactuation of the real gripper. Our model can be used as a foundation for research in complex grasping and manipulation tasks with the Sake Robotics Gripper.

[1]  Anind K. Dey,et al.  Maximum Entropy Inverse Reinforcement Learning , 2008, AAAI.

[2]  Hong Liu,et al.  Multisensory five-finger dexterous hand: The DLR/HIT Hand II , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[3]  Jan Peters,et al.  Learning robot in-hand manipulation with tactile features , 2015, 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids).

[4]  Vicenç Gómez,et al.  Optimal control as a graphical model inference problem , 2009, Machine Learning.

[5]  Vincent Padois,et al.  Tools for simulating humanoid robot dynamics: A survey based on user feedback , 2014, 2014 IEEE-RAS International Conference on Humanoid Robots.

[6]  Marc Toussaint,et al.  Learned graphical models for probabilistic planning provide a new class of movement primitives , 2013, Front. Comput. Neurosci..

[7]  P. Dario,et al.  From "macro" to "micro" manipulation: models and experiments , 2004, IEEE/ASME Transactions on Mechatronics.

[8]  David J. Montana,et al.  The kinematics of contact with compliance , 1989, Proceedings, 1989 International Conference on Robotics and Automation.

[9]  Lucas Nogueira Comparative Analysis Between Gazebo and V-REP Robotic Simulators , 2014 .

[10]  Tamim Asfour,et al.  Toward humanoid manipulation in human-centred environments , 2008, Robotics Auton. Syst..

[11]  Jan Peters,et al.  Experiments with Hierarchical Reinforcement Learning of Multiple Grasping Policies , 2016, ISER.

[12]  T. Flash,et al.  The coordination of arm movements: an experimentally confirmed mathematical model , 1985, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[13]  David J. Montana,et al.  Contact stability for two-fingered grasps , 1992, IEEE Trans. Robotics Autom..

[14]  Jan Peters,et al.  Guiding Trajectory Optimization by Demonstrated Distributions , 2017, IEEE Robotics and Automation Letters.

[15]  Siddhartha S. Srinivasa,et al.  The YCB object and Model set: Towards common benchmarks for manipulation research , 2015, 2015 International Conference on Advanced Robotics (ICAR).

[16]  Marco Ceccarelli,et al.  An Optimization Problem Algorithm for Kinematic Design of Mechanisms for Two-Finger Grippers , 2009 .

[17]  John F. Canny,et al.  Planning optimal grasps , 1992, Proceedings 1992 IEEE International Conference on Robotics and Automation.

[18]  Jan Peters,et al.  Using probabilistic movement primitives in robotics , 2018, Auton. Robots.

[19]  Jianwei Zhang,et al.  Learning to grasp everyday objects using reinforcement-learning with automatic value cut-off , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[20]  Kostas J. Kyriakopoulos,et al.  Minimum jerk path generation , 1988, Proceedings. 1988 IEEE International Conference on Robotics and Automation.

[21]  Danica Kragic,et al.  Interactive grasp learning based on human demonstration , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[22]  Yoji Umetani,et al.  The Development of Soft Gripper for the Versatile Robot Hand , 1978 .