102 citations · 204 across the 61 of their papers we have counts for
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Sampling-Aware Control Barrier Functions for Safety-Critical and Finite-Time Constrained Control
Shuo Liu, Wei Xiao, Calin A. Belta
In safety-critical control systems, ensuring both safety and feasibility under sampled-data implementations is crucial for practical deployment. Existing Control Barrier Function (…
Learning Safety for Obstacle Avoidance via Control Barrier Functions
Shuo Liu, Zhe Huang, Calin A. Belta
Obstacle avoidance is central to safe navigation, especially for robots with arbitrary and nonconvex geometries operating in cluttered environments. Existing Control Barrier Functi…
STL-based Optimization of Biomolecular Neural Networks for Regression and Control
Eric Palanques-Tost, Hanna Krasowski, Murat Arcak +2
Biomolecular Neural Networks (BNNs), artificial neural networks with biologically synthesizable architectures, achieve universal function approximation capabilities beyond simple b…
SPLASH! Sample-efficient Preference-based inverse reinforcement learning for Long-horizon Adversarial tasks from Suboptimal Hierarchical demonstrations
Peter Crowley, Zachary Serlin, Tyler Paine +3
Inverse Reinforcement Learning (IRL) presents a powerful paradigm for learning complex robotic tasks from human demonstrations. However, most approaches make the assumption that ex…
Safety-Aware Reinforcement Learning for Control via Risk-Sensitive Action-Value Iteration and Quantile Regression
Clinton Enwerem, Aniruddh G. Puranic, John S. Baras +1
Mainstream approximate action-value iteration reinforcement learning (RL) algorithms suffer from overestimation bias, leading to suboptimal policies in high-variance stochastic env…
Accelerated Learning with Linear Temporal Logic using Differentiable Simulation
Alper Kamil Bozkurt, Calin Belta, Ming C. Lin
Ensuring that reinforcement learning (RL) controllers satisfy safety and reliability constraints in real-world settings remains challenging: state-avoidance and constrained Markov…