[CoRL 2020 Best Paper Nominee] Learning from Suboptimal Demonstration via Self-Supervised Reward Regression

CoRL 2020

[CoRL 2020 Best Paper Nominee] Learning from Suboptimal Demonstration via Self-Supervised Reward Regression

Dec 18, 2020
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"**Learning from Suboptimal Demonstration via Self-Supervised Reward Regression** Letian Chen (Georgia Institute of Technology)*; Rohan Paleja (Georgia Institute of Technology); Matthew Gombolay (Georgia Institute of Technology) Publication: http://corlconf.github.io/paper_281/ **Abstract** Learning from Demonstration (LfD) seeks to democratize robotics by enabling non-roboticist end-users to teach robots to perform a task by providing a human demonstration. However, modern LfD techniques, e.g. inverse reinforcement learning (IRL), assume users provide at least stochastically optimal demonstrations. This assumption fails to hold in most real-world scenarios. Recent attempts to learn from sub-optimal demonstration leverage pairwise rankings and following the Luce-Shepard rule. However, we show these approaches make incorrect assumptions and thus suffer from brittle, degraded performance. We overcome these limitations in developing a novel approach that bootstraps off suboptimal demonstrations to synthesize optimality-parameterized data to train an idealized reward function. We empirically validate we learn an idealized reward function with ~0.95 correlation with ground-truth reward versus ~0.75 for prior work. We can then train policies achieving ~200% improvement over the suboptimal demonstration and ~90% improvement over prior work. We present a physical demonstration of teaching a robot a topspin strike in table tennis that achieves 32% faster returns and 40% more topspin than user demonstration."

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