Benchmarking Reinforcement Learning Methods for Dexterous Robotic Manipulation with a Three-Fingered Gripper
<|reference_start|>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 results of three RL algorithms trained on intricate in-hand manipulation tasks within practical real-world contexts are presented. Our study not only demonstrates the practicality of RL training in authentic real-world scenarios, facilitating direct real-world applications, but also provides insights into the associated challenges and considerations. Additionally, our experiences with the employed experimental methods are shared, with the aim of empowering and engaging fellow researchers and practitioners in this dynamic field of robotics.<|reference_end|>
@article{cutler2024benchmarking,
    title={Benchmarking Reinforcement Learning Methods for Dexterous Robotic
  Manipulation with a Three-Fingered Gripper},
    author={Elizabeth Cutler, Yuning Xing, Tony Cui, Brendan Zhou, Koen van
  Rijnsoever, Ben Hart, David Valencia, Lee Violet C. Ong, Trevor Gee, Minas
  Liarokapis, Henry Williams},
    journal={Australasian conference on robotics and automation (ACRA 2023)},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.14747},
    primaryClass={cs.RO cs.AI cs.LG}
}