14 citations · 35 across the 21 of their papers we have counts for
5 papers · 1 filter
Safety Filtering While Training: Improving the Performance and Sample Efficiency of Reinforcement Learning Agents
Federico Pizarro Bejarano, Lukas Brunke, Angela P. Schoellig
Reinforcement learning (RL) controllers are flexible and performant but rarely guarantee safety. Safety filters impart hard safety guarantees to RL controllers while maintaining fl…
Semantically Safe Robot Manipulation: From Semantic Scene Understanding to Motion Safeguards
Lukas Brunke, Yanni Zhang, Ralf Römer +4
Ensuring safe interactions in human-centric environments requires robots to understand and adhere to constraints recognized by humans as "common sense" (e.g., "moving a cup of wate…
Preventing Inactive CBF Safety Filters Caused by Invalid Relative Degree Assumptions
Lukas Brunke, Siqi Zhou, Angela P. Schoellig
Control barrier function (CBF) safety filters emerged as a popular framework to certify and modify potentially unsafe control inputs, for example, provided by a reinforcement learn…
Practical Considerations for Discrete-Time Implementations of Continuous-Time Control Barrier Function-Based Safety Filters
Lukas Brunke, Siqi Zhou, Mingxuan Che +1
Safety filters based on control barrier functions (CBFs) have become a popular method to guarantee safety for uncertified control policies, e.g., as resulting from reinforcement le…
Is Data All That Matters? The Role of Control Frequency for Learning-Based Sampled-Data Control of Uncertain Systems
Ralf Römer, Lukas Brunke, Siqi Zhou +1
Learning models or control policies from data has become a powerful tool to improve the performance of uncertain systems. While a strong focus has been placed on increasing the amo…