Johannes Twiefel

About

Johannes Twiefel is a leading researcher in human-robot interaction (HRI), specializing in making robots more adaptive, personal, and intuitive through natural language and multimodal learning. His work centers on integrating speech recognition, reinforcement learning, and personalization to create robots that can learn from and respond to human users in real-world domestic and educational settings. Twiefel’s most influential paper, “The Impact of Personalisation on Human-Robot Interaction in Learning Scenarios” (66 citations), demonstrates how adaptive dialogue systems enable more natural, user-centered interactions. He also pioneered the use of interactive reinforcement learning guided by speech, as shown in his 2015 work on training a cleaning robot through verbal feedback (56 citations). To overcome limitations of cloud-based speech recognition, he developed domain-dependent phonetic post-processing (45 citations), significantly improving robot comprehension in specific tasks. Twiefel contributed to the development of the Neuro-Inspired Companion (NICO) robot, which integrates memory, face tracking, and personalized conversation. His research has been recognized internationally, with over 280 total citations, and continues to shape the future of socially intelligent, language-capable robots that can learn alongside humans.

Research Focus

Key Achievements

Top Papers

1. 1
   The Impact of Personalisation on Human-Robot Interaction in Learning Scenarios

   66 citations · 2017
2. 2
   Interactive reinforcement learning through speech guidance in a domestic scenario

   56 citations · 2015
3. 3
   Improving Domain-independent Cloud-Based Speech Recognition with Domain-Dependent Phonetic Post-Processing

   45 citations · 2014
4. 4
   Multi-modal integration of dynamic audiovisual patterns for an interactive reinforcement learning scenario

   35 citations · 2016
5. 5
   Using natural language feedback in a neuro-inspired integrated multimodal robotic architecture

   20 citations · 2016
6. 6
   A Robotic Home Assistant with Memory Aid Functionality

   16 citations · 2016
7. 7
   Hey robot, why don't you talk to me?

   16 citations · 2017
8. 8
   Teach Your Robot Your Language! Trainable Neural Parser for Modeling Human Sentence Processing: Examples for 15 Languages

   16 citations · 2019
9. 9
   Object Learning with Natural Language in a Distributed Intelligent System: A Case Study of Human-Robot Interaction

   8 citations · 2013
10. 10
    Semantic Role Labelling for Robot Instructions using Echo State Networks

    6 citations · 2016

Key Collaborators

Contact & Links