Lessons from Learning to Spin "Pens"
<|reference_start|>Lessons from Learning to Spin "Pens": In-hand manipulation of pen-like objects is an important skill in our daily lives, as many tools such as hammers and screwdrivers are similarly shaped. However, current learning-based methods struggle with this task due to a lack of high-quality demonstrations and the significant gap between simulation and the real world. In this work, we push the boundaries of learning-based in-hand manipulation systems by demonstrating the capability to spin pen-like objects. We first use reinforcement learning to train an oracle policy with privileged information and generate a high-fidelity trajectory dataset in simulation. This serves two purposes: 1) pre-training a sensorimotor policy in simulation; 2) conducting open-loop trajectory replay in the real world. We then fine-tune the sensorimotor policy using these real-world trajectories to adapt it to the real world dynamics. With less than 50 trajectories, our policy learns to rotate more than ten pen-like objects with different physical properties for multiple revolutions. We present a comprehensive analysis of our design choices and share the lessons learned during development.<|reference_end|>
@article{wang2024lessons,
    title={Lessons from Learning to Spin "Pens"},
    author={Jun Wang, Ying Yuan, Haichuan Che, Haozhi Qi, Yi Ma, Jitendra Malik,
  Xiaolong Wang},
    journal={arXiv preprint arXiv:2407.18902},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.18902},
    primaryClass={cs.RO cs.AI cs.LG}
}