Publications

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 …

Incorporating End-to-End Speech Recognition Models for Sentiment Analysis

Previous work on emotion recognition demonstrated a synergistic effect of combining several modalities such as auditory, visual, and …

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 …

On the Robustness of Speech Emotion Recognition for Human-Robot Interaction with Deep Neural Networks

Speech emotion recognition (SER) is an important aspect of effective human-robot collaboration and received a lot of attention from the …

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 …

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 …

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 …

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 …

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 …

Simultaneous Human-Robot Adaptation for Effective Skill Transfer

In this paper, we propose and implement a human-in-the loop robot skill synthesis framework that involves simultaneous adaptation of …

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 …