Learning Stable Robot Grasping with Transformer-based Tactile Control Policies
<|reference_start|>Learning Stable Robot Grasping with Transformer-based Tactile Control Policies: Measuring grasp stability is an important skill for dexterous robot manipulation tasks, which can be inferred from haptic information with a tactile sensor. Control policies have to detect rotational displacement and slippage from tactile feedback, and determine a re-grasp strategy in term of location and force. Classic stable grasp task only trains control policies to solve for re-grasp location with objects of fixed center of gravity. In this work, we propose a revamped version of stable grasp task that optimises both re-grasp location and gripping force for objects with unknown and moving center of gravity. We tackle this task with a model-free, end-to-end Transformer-based reinforcement learning framework. We show that our approach is able to solve both objectives after training in both simulation and in a real-world setup with zero-shot transfer. We also provide performance analysis of different models to understand the dynamics of optimizing two opposing objectives.<|reference_end|>
@article{puang2024learning,
    title={Learning Stable Robot Grasping with Transformer-based Tactile Control
  Policies},
    author={En Yen Puang, Zechen Li, Chee Meng Chew, Shan Luo, Yan Wu},
    journal={arXiv preprint arXiv:2407.21172},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.21172},
    primaryClass={cs.RO}
}