A method for imitation learning that learns latent success-failure discrepancy representations in attention and uses an attention-based metric to select beneficial failure demonstrations for improved task performance in simulation.
Learning from imperfect demonstrations with self-supervision for robotic manipulation
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.RO 1years
2026 1verdicts
UNVERDICTED 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
How to Utilize Failure Demo Data?: Effective Data Selection for Imitation Learning Using Distribution Differences in Attention Mechanism
A method for imitation learning that learns latent success-failure discrepancy representations in attention and uses an attention-based metric to select beneficial failure demonstrations for improved task performance in simulation.