Reinforcement Learning

Deep reinforcement learning using compositional representations for performing instructions

Spoken language is one of the most efficient ways to instruct robots about performing domestic tasks. However, the state of the environment has to be considered to plan and execute actions successfully. We propose a system that learns to recognise …

Neural End-to-End Learning of Reach for Grasp Ability with a 6-DoF Robot Arm

We present a neural end-to-end learning approach for a reach-for-grasp task on an industrial UR5 arm. Our approach combines the generation of suitable training samples by classical inverse kinematics (IK) solvers in a simulation environment in …

Language-modulated Actions using Deep Reinforcement Learning for Safer Human-Robot Interaction

Spoken language can be an efficient and intuitive way to warn robots about threats. Guidance and warnings from a human can be used to inform and modulate a robot’s actions. An open research question is how the instructions and warnings can be …

Learning Spatial Representation for Safe Human-Robot Collaboration in Joint Manual Tasks

Programming robots for a safe interaction with humans is extremely complex especially in collaborative tasks. One reason is the unpredictable behaviour of humans that may have an intention which is not clear to the robot. We present a novel …

EmoRL: Real-time Acoustic Emotion Classification using Deep Reinforcement Learning

Acoustically expressed emotions can make communication with a robot more efficient. Detecting emotions like anger could provide a clue for the robot indicating unsafe/undesired situations. Recently, several deep neural network-based models have been …

Language-modulated Safer Actions using Deep Reinforcement Learning

Programming robots for a safe interaction with humans is extremely complex especially in collaborative tasks. One reason is the unpredictable behaviour of humans that may have an intention which is not clear to the robot. We present a novel …

Accelerating Deep Continuous Reinforcement Learning through Task Simplification

Robotic motor policies can, in theory, be learned via deep continuous reinforcement learning. In practice, however, collecting the enormous amount of required training samples in realistic time, surpasses the possibilities of many robotic platforms. …

Deep Reinforcement Learning using Symbolic Representation for Performing Spoken Language Instructions

Spoken language is one of the most efficient ways to instruct robots about performing domestic tasks. However, the state of the environment has to be considered to plan and execute the actions successfully. We propose a system which can learn to …