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LazyDAgger: Reducing Context Switching in Interactive Imitation Learning
[article]
2021
arXiv
pre-print
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We present LazyDAgger, which extends the interactive imitation learning (IL) algorithm SafeDAgger to reduce context switches between supervisor and autonomous control. ...
In physical fabric manipulation experiments with an ABB YuMi robot, LazyDAgger reduces context switches by 60% while achieving a 60% higher success rate than SafeDAgger at execution time. ...
However, a key challenge in interactive imitation learning is to reduce the burden that interventions place on the human supervisor [24, 53] . ...
arXiv:2104.00053v2
fatcat:a4w4gtrh35b6pn774j45ohu22e
ThriftyDAgger: Budget-Aware Novelty and Risk Gating for Interactive Imitation Learning
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Effective robot learning often requires online human feedback and interventions that can cost significant human time, giving rise to the central challenge in interactive imitation learning: is it possible ...
ThriftyDAgger uses a learned switching policy to solicit interventions only at states that are sufficiently (1) novel, where the robot policy has no reference behavior to imitate, or (2) risky, where the ...
The authors were supported in part by the Scalable Collaborative Human-Robot Learning (SCHooL) Project, NSF National Robotics Initiative Award 1734633, and by donations from Google, Siemens, Amazon Robotics ...
arXiv:2109.08273v1
fatcat:uvbfrpd3tbhqxbqvfgkjhvvxn4