Instructing Robots by Sketching: Learning from Demonstration via Probabilistic Diagrammatic Teaching
Weiming Zhi, Tianyi Zhang, Matthew Johnson-Roberson

TL;DR
This paper introduces Diagrammatic Teaching, a novel Learning from Demonstration paradigm where users sketch trajectories on 2D images to teach robots, utilizing a probabilistic model and ray-tracing to synthesize 3D motions, validated on simulation and real robots.
Contribution
It proposes a new intuitive LfD method using sketches on images and develops the RPTL framework to convert these sketches into 3D robot trajectories.
Findings
Effective in simulation and real robot experiments.
Able to generate trajectories that mimic user sketches.
Works with fixed-base and quadruped-mounted manipulators.
Abstract
Learning for Demonstration (LfD) enables robots to acquire new skills by imitating expert demonstrations, allowing users to communicate their instructions in an intuitive manner. Recent progress in LfD often relies on kinesthetic teaching or teleoperation as the medium for users to specify the demonstrations. Kinesthetic teaching requires physical handling of the robot, while teleoperation demands proficiency with additional hardware. This paper introduces an alternative paradigm for LfD called Diagrammatic Teaching. Diagrammatic Teaching aims to teach robots novel skills by prompting the user to sketch out demonstration trajectories on 2D images of the scene, these are then synthesised as a generative model of motion trajectories in 3D task space. Additionally, we present the Ray-tracing Probabilistic Trajectory Learning (RPTL) framework for Diagrammatic Teaching. RPTL extracts…
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Taxonomy
TopicsRobot Manipulation and Learning · Human Motion and Animation · Human Pose and Action Recognition
