User profiles for Lukas Brunke
Lukas BrunkePhD Candidate, University of Toronto and Technical University of Munich Verified email at mail.utoronto.ca Cited by 866 |
Safe learning in robotics: From learning-based control to safe reinforcement learning
The last half decade has seen a steep rise in the number of contributions on safe learning
methods for real-world robotic deployments from both the control and reinforcement learning …
methods for real-world robotic deployments from both the control and reinforcement learning …
Learning Model Predictive Control for Competitive Autonomous Racing
L Brunke�- arXiv preprint arXiv:2005.00826, 2020 - arxiv.org
The goal of this thesis is to design a learning model predictive controller (LMPC) that allows
multiple agents to race competitively on a predefined race track in real-time. This thesis …
multiple agents to race competitively on a predefined race track in real-time. This thesis …
[PDF][PDF] Safe-control-gym: A unified benchmark suite for safe learning-based control and reinforcement learning
In recent years, both reinforcement learning and learning-based control—as well as the
study of their safety, which is crucial for deployment in real-world robots—have gained …
study of their safety, which is crucial for deployment in real-world robots—have gained …
RLO-MPC: Robust learning-based output feedback MPC for improving the performance of uncertain systems in iterative tasks
In this work we address the problem of performing a repetitive task when we have uncertain
observations and dynamics. We formulate this problem as an iterative infinite horizon …
observations and dynamics. We formulate this problem as an iterative infinite horizon …
Barrier bayesian linear regression: Online learning of control barrier conditions for safety-critical control of uncertain systems
In this work, we consider the problem of designing a safety filter for a nonlinear uncertain
control system. Our goal is to augment an arbitrary controller with a safety filter such that the …
control system. Our goal is to augment an arbitrary controller with a safety filter such that the …
Safe-Control-Gym: A Unified Benchmark Suite for Safe Learning-Based Control and Reinforcement Learning in Robotics
In recent years, both reinforcement learning and learning-based control—as well as the
study of their safety , which is crucial for deployment in real-world robots—have gained …
study of their safety , which is crucial for deployment in real-world robots—have gained …
Robust predictive output-feedback safety filter for uncertain nonlinear control systems
In real-world applications, we often require reliable decision making under dynamics
uncertainties using noisy high-dimensional sensory data. Recently, we have seen an increasing …
uncertainties using noisy high-dimensional sensory data. Recently, we have seen an increasing …
Optimized control invariance conditions for uncertain input-constrained nonlinear control systems
Providing safety guarantees for learning-based controllers is important for real-world
applications. One approach to realizing safety for arbitrary control policies is safety filtering. If …
applications. One approach to realizing safety for arbitrary control policies is safety filtering. If …
Robust adaptive model predictive control for guaranteed fast and accurate stabilization in the presence of model errors
Numerous control applications, including robotic systems such as unmanned aerial vehicles
or assistive robots, are expected to guarantee high performance despite being deployed in …
or assistive robots, are expected to guarantee high performance despite being deployed in …
Semantically Safe Robot Manipulation: From Semantic Scene Understanding to Motion Safeguards
Ensuring safe interactions in human-centric environments requires robots to understand
and adhere to constraints recognized by humans as "common sense" (eg, "moving a cup of …
and adhere to constraints recognized by humans as "common sense" (eg, "moving a cup of …