A Computationally Efficient Nonparametric Approach for Robot Imitation Learning

Abstract

Transferring human skills to robots through learning from demonstrations has been an important topic in the robotics community, and many models have been developed for learning and adapting such skills. Among them, nonparametric representations are an appealing choice, since nonparametric solutions alleviate the explicit definition of basis functions, require fewer hyperparameters, and facilitate straightforward generalization for tasks involving high-dimensional inputs (e.g., human-robot collaboration and dual-arm manipulation). However, a commonly raised concern for nonparametric models is their computational complexity. In this paper, we propose a computationally efficient solution for nonparametric skill learning, whose computation time grows quadratically with the length of demonstrations, as opposed to the cubic growth in a standard nonparametric model. The solution is further improved by exploiting local models and fusing their predictions. We evaluate our approach in a 2-D writing task with time input, a 3-D human-guided obstacle avoidance task, and a dual-arm transportation task associated with 7-D input. The results show that our solution achieves comparable performance to the parametric method and enables instant adaptations in tasks associated with time or multi-dimensional inputs.