Benchmarking Reinforcement Learning Methods for Dexterous Robotic
Manipulation with a Three-Fingered Gripper
Benchmarking Reinforcement Learning Methods for Dexterous Robotic
Manipulation with a Three-Fingered Gripper
Reinforcement Learning (RL) training is predominantly conducted in cost-effective and controlled simulation environments. However, the transfer of these trained models to real-world tasks often presents unavoidable challenges. This research explores the direct training of RL algorithms in controlled yet realistic real-world settings for the execution of dexterous manipulation. The benchmarking …