Sketch-to-Skill: Bootstrapping Robot Learning with Human Drawn Trajectory Sketches

Abstract

Training robotic manipulation policies traditionally requires numerous demonstrations and/or environmental rollouts.While recent Imitation Learning (IL) and Reinforcement Learning (RL) methods have reduced the number of required demonstrations, they still rely on expert knowledge to collect high-quality data, limiting scalability and accessibility.We propose SKETCH-TO-SKILL, a novel framework that leverages human-drawn 2D sketch trajectories to bootstrap and guide RL for robotic manipulation.Our approach extends beyond previous sketch-based methods, which were primarily focused on imitation learning or policy conditioning, limited to specific trained tasks.SKETCH-TO-SKILL employs a Sketch-to-3D Trajectory Generator that translates 2D sketches into 3D trajectories, which are then used to autonomously collect initial demonstrations.We utilize these sketch-generated demonstrations in two ways: to pre-train an initial policy through behavior cloning and to refine this policy through RL with guided exploration.Experimental results demonstrate that SKETCH-TO-SKILL achieves 96% of the performance of the baseline model that leverages teleoperated demonstration data, while exceeding the performance of a pure reinforcement learning policy by 170%, only from sketch inputs.This makes robotic manipulation learning more accessible and potentially broadens its applications across various domains.