  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.
