Learning to Grasp from a Single Demonstration

no code implementations • 9 Jun 2018 • Pieter Van Molle, Tim Verbelen, Elias De Coninck, Cedric De Boom, Pieter Simoens, Bart Dhoedt

Learning-based approaches for robotic grasping using visual sensors typically require collecting a large size dataset, either manually labeled or by many trial and errors of a robotic manipulator in the real or simulated world.

Robotic Grasping

Sensor Fusion for Robot Control through Deep Reinforcement Learning

no code implementations • 13 Mar 2017 • Steven Bohez, Tim Verbelen, Elias De Coninck, Bert Vankeirsbilck, Pieter Simoens, Bart Dhoedt

Deep reinforcement learning is becoming increasingly popular for robot control algorithms, with the aim for a robot to self-learn useful feature representations from unstructured sensory input leading to the optimal actuation policy.

reinforcement-learning Reinforcement Learning (RL) +1

Lazy Evaluation of Convolutional Filters

no code implementations • 27 May 2016 • Sam Leroux, Steven Bohez, Cedric De Boom, Elias De Coninck, Tim Verbelen, Bert Vankeirsbilck, Pieter Simoens, Bart Dhoedt

In this paper we propose a technique which avoids the evaluation of certain convolutional filters in a deep neural network.