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Found 11 papers, 6 papers with code
Practical Considerations for Discrete-Time Implementations of Continuous-Time Control Barrier Function-Based Safety Filters
In particular, we look at the issues caused by discrete-time implementations of the continuous-time CBF-based safety filter, especially for cases where the magnitude of the Lie derivative of the CBF with respect to the control input is zero or close to zero.
Is Data All That Matters? The Role of Control Frequency for Learning-Based Sampled-Data Control of Uncertain Systems
While a strong focus has been placed on increasing the amount and quality of data to improve performance, data can never fully eliminate uncertainty, making feedback necessary to ensure stability and performance.
Optimized Control Invariance Conditions for Uncertain Input-Constrained Nonlinear Control Systems
We demonstrate the efficacy of our proposed approach in simulation and real-world experiments on a quadrotor and show that we can achieve safe closed-loop behavior for a learned system while satisfying state and input constraints.
Multi-Step Model Predictive Safety Filters: Reducing Chattering by Increasing the Prediction Horizon
In experiments with a Crazyflie 2. 0 drone, we show that, in addition to preserving the desired safety guarantees, the proposed MPSF reduces chattering by more than a factor of 4 compared to previous MPSF formulations.
What is the Impact of Releasing Code with Publications? Statistics from the Machine Learning, Robotics, and Control Communities
Open-sourcing research publications is a key enabler for the reproducibility of studies and the collective scientific progress of a research community.
Robust Predictive Output-Feedback Safety Filter for Uncertain Nonlinear Control Systems
Recently, we have seen an increasing number of learning-based control algorithms developed to address the challenge of decision making under dynamics uncertainties.
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.
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 significant traction.
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 communities.
Evaluating Input Perturbation Methods for Interpreting CNNs and Saliency Map Comparison
Input perturbation methods occlude parts of an input to a function and measure the change in the function's output.
Learning Model Predictive Control for Competitive Autonomous Racing
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.
