Fast Explicit-Input Assistance for Teleoperation in Clutter

Abstract

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: github.com/NVlabs/fast-explicit-teleop.

BibTeX Entry

@article{walker2024explicit,
  author = {Walker, Nick and Yang, Xuning and Garg, Animesh and Cakmak, Maya and Fox, Dieter and P\'{e}rez-D'Arpino, Claudia},
  title = {Fast Explicit-Input Assistance for Teleoperation in Clutter},
  location = {Abu Dhabi, UAE},
  year = {2024},
  month = oct,
  booktitle = {2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  wwwtype = {conference},
  wwwpdf = {https://arxiv.org/pdf/2402.02612.pdf},
  wwwcode = {https://github.com/NVlabs/fast-explicit-teleop}
}