Now showing items 1-4 of 4
Using Dimensionality Reduction to Exploit Constraints in Reinforcement Learning
Reinforcement learning in the high-dimensional, continuous spaces typical in robotics, remains a challenging problem. To overcome this challenge, a popular approach has been to use demonstrations to find an appropriate ...
Learning Nullspace Policies
Many everyday tasks performed by people, such as reaching, pointing or drawing, resolve redundant degrees of freedom in the arm in a similar way. In this paper we present a novel method for learning the strategy used ...
Methods for Learning Control Policies from Variable Constraint Demonstrations
Many everyday human skills can be framed in terms of performing some task subject to constraints imposed by the task or the environment. Constraints are usually not observable and frequently change between contexts. In ...
Transferring Impedance Control Strategies Between Heterogeneous Systems via Apprenticeship Learning
We present a novel method for designing controllers for robots with variable impedance actuators. We take an imitation learning approach, whereby we learn impedance modulation strategies from observations of behaviour (for ...