Active Learning based on Data Uncertainty and Model Sensitivity
This addresses a key challenge in robotics for enabling safer and more reliable skill acquisition, though it appears incremental as it builds on existing active learning and deep generative model techniques.
The paper tackles the problem of robots failing to detect missing knowledge during skill generalization or transitions, which leads to abrupt movements or collisions, by introducing a novel active learning algorithm that quantifies uncertainty and requests additional demonstrations to generate smooth trajectories, demonstrating efficacy on simulated tasks with a pendulum and a 7-DoF arm.
Robots can rapidly acquire new skills from demonstrations. However, during generalisation of skills or transitioning across fundamentally different skills, it is unclear whether the robot has the necessary knowledge to perform the task. Failing to detect missing information often leads to abrupt movements or to collisions with the environment. Active learning can quantify the uncertainty of performing the task and, in general, locate regions of missing information. We introduce a novel algorithm for active learning and demonstrate its utility for generating smooth trajectories. Our approach is based on deep generative models and metric learning in latent spaces. It relies on the Jacobian of the likelihood to detect non-smooth transitions in the latent space, i.e., transitions that lead to abrupt changes in the movement of the robot. When non-smooth transitions are detected, our algorithm asks for an additional demonstration from that specific region. The newly acquired knowledge modifies the data manifold and allows for learning a latent representation for generating smooth movements. We demonstrate the efficacy of our approach on generalising elementary skills, transitioning across different skills, and implicitly avoiding collisions with the environment. For our experiments, we use a simulated pendulum where we observe its motion from images and a 7-DoF anthropomorphic arm.