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...

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Bibliographic Details
Published inarXiv.org
Main Authors Walker, Nick, Yang, Xuning, Garg, Animesh, Cakmak, Maya, Fox, Dieter, Pérez-D'Arpino, Claudia
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 07.10.2024
Subjects
Algorithms
Clutter
Completion time
End effectors
Interactive control
Operators
Robots
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Summary: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
ISSN:2331-8422