Publikation
Fast Kinodynamic Planning on the Constraint Manifold With Deep Neural Networks
Piotr Kicki; Puze Liu; Davide Tateo; Haitham Bou-Ammar; Krzysztof Walas; Piotr Skrzypczynski; Jan Peters
In: IEEE Transactions on Robotics (T-RO), Vol. 40, Pages 277-297, IEEE, 2024.
Zusammenfassung
Motion planning is a mature area of research in
robotics with many well-established methods based on optimiza-
tion or sampling the state space, suitable for solving kinematic
motion planning. However, when dynamic motions under con-
straints are needed and computation time is limited, fast kino-
dynamic planning on the constraint manifold is indispensable.
In recent years, learning-based solutions have become alterna-
tives to classical approaches, but they still lack comprehensive
handling of complex constraints, such as planning on a lower-
dimensional manifold of the task space while considering the
robot’s dynamics. This paper introduces a novel learning-to-
plan framework that exploits the concept of constraint manifold,
including dynamics, and neural planning methods. Our approach
generates plans satisfying an arbitrary set of constraints and
computes them in a short constant time, namely the inference
time of a neural network. This allows the robot to plan and
replan reactively, making our approach suitable for dynamic
environments. We validate our approach on two simulated tasks
and in a demanding real-world scenario, where we use a Kuka
LBR Iiwa 14 robotic arm to perform the hitting movement in
robotic Air Hockey.