132 citations · 543 across the 32 of their papers we have counts for
6 papers · 1 filter
Coarse-to-Fine Q-attention: Efficient Learning for Visual Robotic Manipulation via Discretisation
Stephen James, Kentaro Wada, Tristan Laidlow +1
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 methods…
Q-attention: Enabling Efficient Learning for Vision-based Robotic Manipulation
Stephen James, Andrew J. Davison
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 partly d…
Waypoint Planning Networks
Alexandru-Iosif Toma, Hussein Ali Jaafar, Hao-Ya Hsueh +4
With the recent advances in machine learning, path planning algorithms are also evolving; however, the learned path planning algorithms often have difficulty competing with success…
SIMstack: A Generative Shape and Instance Model for Unordered Object Stacks
Zoe Landgraf, Raluca Scona, Tristan Laidlow +3
By estimating 3D shape and instances from a single view, we can capture information about an environment quickly, without the need for comprehensive scanning and multi-view fusion.…
End-to-End Egospheric Spatial Memory
Daniel Lenton, Stephen James, Ronald Clark +1
Spatial memory, or the ability to remember and recall specific locations and objects, is central to autonomous agents' ability to carry out tasks in real environments. However, mos…
Ivy: Templated Deep Learning for Inter-Framework Portability
Daniel Lenton, Fabio Pardo, Fabian Falck +2
We introduce Ivy, a templated Deep Learning (DL) framework which abstracts existing DL frameworks. Ivy unifies the core functions of these frameworks to exhibit consistent call sig…