Active Learning based on Data Uncertainty and Model Sensitivity

TL;DR

This paper presents a novel active learning algorithm using deep generative models and Jacobian-based uncertainty detection to improve robot skill generalization, transition smoothness, and collision avoidance.

Contribution

It introduces a new method combining data uncertainty and model sensitivity for active learning in robotic skill acquisition, enhancing smoothness and safety.

Findings

Effective in generating smooth trajectories

Improves skill transfer and transition between tasks

Implicitly reduces collisions in robotic movements

Abstract

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.