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 time-varying
probability densities from the 2D sketches, applies ray-tracing to find
corresponding regions in 3D Cartesian space, and fits a probabilistic model of
motion trajectories to these regions. New motion trajectories, which mimic
those sketched by the user, can then be generated from the probabilistic model.
We empirically validate our framework both in simulation and on real robots,
which include a fixed-base manipulator and a quadruped-mounted manipulator.

通过对 2D 场景图像进行示范轨迹草图，在三维任务空间中生成运动轨迹的概率模型，以此作为一种新型的 LfD（Learning for Demonstration）范式。