Fast Explicit-Input Assistance for Teleoperation in Clutter
<|reference_start|>Fast Explicit-Input Assistance for Teleoperation in Clutter: The performance of prediction-based assistance for robot teleoperation degrades in unseen or goal-rich environments due to incorrect or quickly-changing intent inferences. Poor predictions can confuse operators or cause them to change their control input to implicitly signal their goal. We present a new assistance interface for robotic manipulation where an operator can explicitly communicate a manipulation goal by pointing the end-effector. The pointing target specifies a region for local pose generation and optimization, providing interactive control over grasp and placement pose candidates. We compare the explicit pointing interface to an implicit inference-based assistance scheme in a within-subjects user study (N=20) where participants teleoperate a simulated robot to complete a multi-step singulation and stacking task in cluttered environments. We find that operators prefer the explicit interface, experience fewer pick failures and report lower cognitive workload. Our code is available at: https://github.com/NVlabs/fast-explicit-teleop<|reference_end|>
@article{walker2024fast,
    title={Fast Explicit-Input Assistance for Teleoperation in Clutter},
    author={Nick Walker, Xuning Yang, Animesh Garg, Maya Cakmak, Dieter Fox,
  Claudia P'erez-D'Arpino},
    journal={arXiv preprint arXiv:2402.02612},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.02612},
    primaryClass={cs.RO}
}