Papers
20
Total Citations
651
H-Index
12
About
Vincent Berenz is a robotics researcher whose work spans robot control, human-robot interaction, and machine learning for dynamic manipulation tasks. His research is united by a drive to make robots more capable, safe, and socially intelligent in real-world environments. Berenz has made significant contributions to robust control and real-time perception in robotics. His 2020 work combining model predictive control with deep neural networks for safe, fast robot manipulation has garnered 221 citations, establishing him as a notable voice in the intersection of classical control theory and modern machine learning. Complementing this, his 2018 paper on integrating real-time perception with reactive motion generation — accumulating 107 citations — demonstrated the critical importance of continuous sensory feedback for robust grasping under uncertainty. His research extends into reinforcement learning for dynamic tasks, with a celebrated 2022 study teaching muscular robots to play table tennis from scratch (86 citations). He has also advanced open-source hardware through the TriFinger platform for dexterous manipulation research, and explored ethical AI behavior in eldercare robotics. Earlier in his career, Berenz pioneered affective human-robot interaction using wearable physiological sensing devices. Together, these contributions reflect a broad and humanistic vision for intelligent, responsive robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Real-Time Perception Meets Reactive Motion Generation107 citations · 2018
- 3Learning to Play Table Tennis From Scratch Using Muscular Robots86 citations · 2022
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- 5
- 6TriFinger: An Open-Source Robot for Learning Dexterity24 citations · 2020
- 7
- 8Coaching robot behavior using continuous physiological affective feedback18 citations · 2011
- 9
- 10TriFinger: An Open-Source Robot for Learning Dexterity16 citations · 2020