Learning Barrier Functions for Constrained Motion Planning with Dynamical Systems
This work addresses motion planning limitations for robots in constrained environments, though it appears incremental as it builds on existing dynamical systems methods.
The paper tackled the problem of planning robotic motions with workspace constraints by learning constraints from human demonstrations and generating trajectories that respect these bounds, achieving effective results in both simulations and real robot experiments.
Stable dynamical systems are a flexible tool to plan robotic motions in real-time. In the robotic literature, dynamical system motions are typically planned without considering possible limitations in the robot's workspace. This work presents a novel approach to learn workspace constraints from human demonstrations and to generate motion trajectories for the robot that lie in the constrained workspace. Training data are incrementally clustered into different linear subspaces and used to fit a low dimensional representation of each subspace. By considering the learned constraint subspaces as zeroing barrier functions, we are able to design a control input that keeps the system trajectory within the learned bounds. This control input is effectively combined with the original system dynamics preserving eventual asymptotic properties of the unconstrained system. Simulations and experiments on a real robot show the effectiveness of the proposed approach.