Wilko Schwarting
Papers
6
Total Citations
216
H-Index
5
About
Wilko Schwarting is a leading researcher at the intersection of robotics, reinforcement learning, and autonomous systems, with a focus on enabling safe and intelligent interactions in mixed human-robot environments. His work spans soft robotics, multi-agent coordination, and control theory, where he has made significant contributions to both perception and decision-making. Schwarting’s most influential paper, "Learning Object Grasping for Soft Robot Hands" (2018, 172 citations), introduced a 3D CNN approach that leverages the compliance of soft hands to handle uncertainty, setting a benchmark for dexterous manipulation. He further advanced control theory with "Is Bang-Bang Control All You Need?" (2021), which challenged conventional RL paradigms by showing that Bernoulli policies can effectively solve continuous control tasks. His research on cooperative localization and stochastic dynamic games has been critical for autonomous driving, addressing nonlinear observability and belief-space planning. Schwarting’s work has garnered over 200 citations, reflecting its impact on both academic and applied robotics. His recent "OptFlow" (2024) offers a fast, unsupervised scene flow estimation method, promising to enhance 3D perception in autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1Learning Object Grasping for Soft Robot Hands172 citations · 2018
- 2
- 3Range-based Cooperative Localization with Nonlinear Observability Analysis12 citations · 2019
- 4Stochastic Dynamic Games in Belief Space8 citations · 2021
- 5
- 6Learning and control for interactions in mixed human-robot environments2 citations · 2021