Learning from Suboptimal Demonstration via Self-Supervised Reward Regression

• URL: http://arxiv.org/abs/2010.11723v3
• Date: Mon, 23 Nov 2020 16:07:38 GMT
• Title: Learning from Suboptimal Demonstration via Self-Supervised Reward Regression
• Authors: Letian Chen, Rohan Paleja, Matthew Gombolay
• Abstract summary: 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. Modern LfD techniques, e.g. inverse reinforcement learning (IRL), assume users provide at least optimalally optimal demonstrations. We show these approaches make incorrect assumptions and thus suffer from brittle, degraded performance. We present a physical demonstration of teaching a robot a topspin strike in table tennis achieves 32% faster returns and 40% more topspin than user demonstration.
• Score: 1.2891210250935146
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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