In our first contribution, we demonstrate the potential of using analytic grasp stability metrics as rewards for RL-based tactile grasp refinement controllers.

Dec 31, 2020 · This paper uses simulated tactile signals and the reinforcement learning (RL) framework to study the sensing needs in grasping systems.

This work demonstrates that analytic grasp stability metrics constitute powerful optimization objectives for RL algorithms that refine grasps on a three- ...

This work demonstrates that analytic grasp stability metrics constitute powerful optimization objectives for RL algorithms that refine grasps on a three- ...

Duration: 2:01
Posted: Sep 23, 2021

This paper uses simulated tactile signals and the reinforcement learning (RL) framework to study the sensing needs in grasping systems. Our first experiment ...

This paper uses simulated tactile signals and the reinforcement learning (RL) framework to study the sensing needs in grasping systems and implies that ...

May 28, 2020 · This work demonstrates that analytic grasp stability metrics constitute powerful optimization objectives for RL algorithms that refine grasps on ...

Tactile Grasp Refinement using Deep Reinforcement Learning and Analytic Grasp Stability Metrics ... This work demonstrates that analytic grasp stability metrics ...

This paper uses simulated tactile signals and the reinforcement learning (RL) framework to study the sensing needs in grasping systems. Our first experiment ...