Task-Based Design and Policy Co-Optimization for Tendon-driven Underactuated Kinematic Chains
<|reference_start|>Task-Based Design and Policy Co-Optimization for Tendon-driven Underactuated Kinematic Chains: Underactuated manipulators reduce the number of bulky motors, thereby enabling compact and mechanically robust designs. However, fewer actuators than joints means that the manipulator can only access a specific manifold within the joint space, which is particular to a given hardware configuration and can be low-dimensional and/or discontinuous. Determining an appropriate set of hardware parameters for this class of mechanisms, therefore, is difficult - even for traditional task-based co-optimization methods. In this paper, our goal is to implement a task-based design and policy co-optimization method for underactuated, tendon-driven manipulators. We first formulate a general model for an underactuated, tendon-driven transmission. We then use this model to co-optimize a three-link, two-actuator kinematic chain using reinforcement learning. We demonstrate that our optimized tendon transmission and control policy can be transferred reliably to physical hardware with real-world reaching experiments.<|reference_end|>
@article{islam2024task-based,
    title={Task-Based Design and Policy Co-Optimization for Tendon-driven
  Underactuated Kinematic Chains},
    author={Sharfin Islam, Zhanpeng He, Matei Ciocarlie},
    journal={arXiv preprint arXiv:2405.14566},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.14566},
    primaryClass={cs.RO}
}