Learning from Demonstration using a Curvature Regularized Variational Auto-Encoder (CurvVAE)

IEEE International Conference on Intelligent Robots and Systems (IROS) 2022 · Travers Rhodes, Tapomayukh Bhattacharjee, and Daniel D. Lee ·

Learning intricate manipulation skills from human demonstrations requires good sample efficiency. We introduce a novel learning algorithm, the Curvature-regularized Variational Auto-Encoder (CurvVAE), to achieve this goal. The CurvVAE is able to model the natural variations in humandemonstrated trajectory data without overfitting. It does so by regularizing the curvature of the learned manifold. To showcase our algorithm, our robot learns an interpretable model of the variation in how humans acquire soft, slippery banana slices with a fork. We evaluate our learned trajectories on a physical robot system, resulting in banana slice acquisition performance better than current state-of-the-art.

PDF Abstract