Fast Explicit-Input Assistance for Teleoperation in Clutter
CoRR(2024)
摘要
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,
resulting in unnatural movement. We present a new assistance algorithm and
interface for robotic manipulation where an operator can explicitly communicate
a manipulation goal by pointing the end-effector. Rapid optimization and
parallel collision checking in a local region around the pointing target enable
direct, interactive control over grasp and place 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,
which improved completion time, pick and place success rates, and NASA TLX
scores. Our code is available at https://github.com/NVlabs/fast-explicit-teleop
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