Despite the success of reinforcement learning methods, they have yet to have their
breakthrough moment when applied to a broad range of robotic manipulation tasks. This is …

We present a coarse-to-fine discretisation method that enables the use of discrete
reinforcement learning approaches in place of unstable and data-inefficient actor-critic …

Manipulation tasks such as preparing a meal or assembling furniture remain highly
challenging for robotics and vision. Traditional task and motion planning (TAMP) methods …

Deep reinforcement learning (RL) algorithms can learn complex robotic skills from raw
sensory inputs, but have yet to achieve the kind of broad generalization and applicability …

Reinforcement learning provides a powerful and flexible framework for automated
acquisition of robotic motion skills. However, applying reinforcement learning requires a …

Learning to solve precision-based manipulation tasks from visual feedback using
Reinforcement Learning (RL) could drastically reduce the engineering efforts required by …

Learning to manipulate 3D objects in an interactive environment has been a challenging
problem in Reinforcement Learning (RL). In particular, it is hard to train a policy that can …

In this paper, we study the problem of learning vision-based dynamic manipulation skills
using a scalable reinforcement learning approach. We study this problem in the context of …

Planning for robotic manipulation requires reasoning about the changes a robot can affect
on objects. When such interactions can be modelled analytically, as in domains with rigid …

The field of visual representation learning has seen explosive growth in the past years, but
its benefits in robotics have been surprisingly limited so far. Prior work uses generic visual …