We present a generalised architecture for reactive mobile manipulation w...
Grasping is the process of picking an object by applying forces and torq...
We investigate how high-resolution tactile sensors can be utilized in
co...
This paper is concerned with perception challenges for robust grasping i...
We propose a novel iterative approach for crossing the reality gap that
...
The DARPA subterranean challenge requires teams of robots to traverse
di...
Dynamic platforms that operate over manyunique terrain conditions typica...
Legged robots often use separate control policies that are highly engine...
Manipulating deformable objects, such as fabric, is a long standing prob...
The large demand for simulated data has made the reality gap a problem o...
We present the Evolved Grasping Analysis Dataset (EGAD), comprising over...
Deep reinforcement learning has been shown to solve challenging tasks wh...
We present a benchmark to facilitate simulated manipulation; an attempt ...
This contribution comprises the interplay between a multi-modal variatio...
When learning behavior, training data is often generated by the learner
...
We quantify the accuracy of various simulators compared to a real world
...
Camera viewpoint selection is an important aspect of visual grasp detect...
Current end-to-end Reinforcement Learning (RL) approaches are severely
l...
We investigate a reinforcement approach for distributed sensing based on...
The application of deep learning in robotics leads to very specific prob...
This paper presents a real-time, object-independent grasp synthesis meth...
A modular method is proposed to learn and transfer visuo-motor policies ...
We present a deep neural network-based method to perform high-precision,...
This paper introduces an end-to-end fine-tuning method to improve hand-e...
While deep learning has had significant successes in computer vision tha...
Robotic challenges like the Amazon Picking Challenge (APC) or the DARPA
...
We generalize Richardson-Lucy (RL) deblurring to 4-D light fields by
rep...
This paper introduces a machine learning based system for controlling a
...