Learning Force Control for Contact-rich Manipulation Tasks with Rigid Position-controlled Robots


Factory automation robot systems often depend on specially-made jigs that precisely position each part, which increases the system’s cost and limits flexibility. We propose a method to determine the 3D pose of an object with high precision and confidence, using only parallel robotic grippers and no parts-specific jigs. Our method automatically generates a sequence of actions that ensures that the real-world position of the physical object matches the system’s assumed pose to sub-mm precision. Furthermore, we propose the use of “extrinsic” actions, which use gravity, the environment and the gripper geometry to significantly reduce or even eliminate the uncertainty about an object’s pose. We show in simulated and real-robot experiments that our method outperforms our previous work, at success rates over 95%.

IEEE Robotics and Automation Letters. Presented in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Cristian Camilo Beltran-Hernandez
Cristian Camilo Beltran-Hernandez

My research interests include reinforcement learning, robot manipulation, and software development for robotics.