REVIEW 1 cited by
Imitation Learning from Imperfect Demonstration
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Imitation learning (IL) aims to learn an optimal policy from demonstrations. However, such demonstrations are often imperfect since collecting optimal ones is costly. To effectively learn from imperfect demonstrations, we propose a novel approach that utilizes confidence scores, which describe the quality of demonstrations. More specifically, we propose two confidence-based IL methods, namely two-step importance weighting IL (2IWIL) and generative adversarial IL with imperfect demonstration and confidence (IC-GAIL). We show that confidence scores given only to a small portion of sub-optimal demonstrations significantly improve the performance of IL both theoretically and empirically.
Forward citations
Cited by 1 Pith paper
-
Beyond Monotonic Progress: Retry-Supervised Value Learning for Robot Imitation
Sparse retry keypoints plus pairwise preference learning yield mistake-sensitive values that reweight mixed-quality demos and raise real-robot imitation success over progress-based baselines.
Discussion (0). Continue with ORCID to comment.