Constrained robot motion planning is a widely used technique to solve complex robot tasks.
We consider the problem of learning representations of constraints from demonstrations with …

Motion planning with constraints is an important part of many real-world robotic systems. In
this work, we study manifold learning methods to learn such constraints from data. We …

Deep reinforcement learning (RL) agents are able to learn contact-rich manipulation tasks
by maximizing a reward signal, but require large amounts of experience, especially in …

Autonomous robots have real-world applications in diverse fields, such as mobile
manipulation and environmental exploration, and many such tasks benefit from a hands-off …

Abstract Gaussian Process Manifold Learning is a novel model based machine learning
method that uses a probabilistic approach to represent a set of data as a manifold. The …

Traditionally, models for control and motion planning were derived from physical properties
of the system. While such a classical approach provides mathematical performance …