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Stochastic and Robust MPC for Bipedal Locomotion: A Comparative Study on Robustness and Performance
Linear Model Predictive Control (MPC) has been successfully used for gen...
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Transparent Concurrency Control: Decoupling Concurrency Control from DBMS
For performance reasons, conventional DBMSes adopt monolithic architectu...
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Declarative vs Rule-based Control for Flocking Dynamics
The popularity of rule-based flocking models, such as Reynolds' classic ...
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Learning a Family of Optimal State Feedback Controllers
Solving optimal control problems is well known to be very computationall...
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MPC for Humanoid Gait Generation: Stability and Feasibility
We present a novel MPC framework for humanoid gait generation which inco...
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Constrained Physics-Informed Deep Learning for Stable System Identification and Control of Linear Systems
This paper presents a novel data-driven method for learning deep constra...
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Trajectory Optimization for Robust Humanoid Locomotion with Sample-Efficient Learning
Trajectory optimization (TO) is one of the most powerful tools for gener...
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Robust walking based on MPC with viability-based feasibility guarantees
Model predictive control (MPC) has shown great success for controlling complex systems such as legged robots. However, when closing the loop, the performance and feasibility of the finite horizon optimal control problem solved at each control cycle is not guaranteed anymore. This is due to model discrepancies, the effect of low-level controllers, uncertainties and sensor noise. To address these issues, we propose a modified version of a standard MPC approach used in legged locomotion with viability (weak forward invariance) guarantees that ensures the feasibility of the optimal control problem. Moreover, we use past experimental data to find the best cost weights, which measure a combination of performance, constraint satisfaction robustness, or stability (invariance). These interpretable costs measure the trade off between robustness and performance. For this purpose, we use Bayesian optimization (BO) to systematically design experiments that help efficiently collect data to learn a cost function leading to robust performance. Our simulation results with different realistic disturbances (i.e. external pushes, unmodeled actuator dynamics and computational delay) show the effectiveness of our approach to create robust controllers for humanoid robots.
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