Ramin Hasani

Model-based versus Model-free Deep Reinforcement Learning for Autonomous Racing Cars.

2021 · arXiv (Cornell University)

Despite the rich theoretical foundation of model-based deep reinforcement learning (RL) agents, their effectiveness in real-world robotics-applications is less studied and understood. In this paper, we, therefore, investigate how such agents generalize to real-world autonomous-vehicle …

BarrierNet: Differentiable Control Barrier Functions for Learning of Safe Robot Control

2023 · IEEE Transactions on Robotics

Many safety-critical applications of neural networks, such as robotic control, require safety guarantees. This article introduces a method for ensuring the safety of learned models for control using differentiable control barrier functions (dCBFs). dCBFs are …