Tsun-Hsuan Wang

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 …

Grounding Language Plans in Demonstrations Through Counterfactual Perturbations

2024 · arXiv (Cornell University)

Grounding the common-sense reasoning of Large Language Models (LLMs) in physical domains remains a pivotal yet unsolved problem for embodied AI. Whereas prior works have focused on leveraging LLMs directly for planning in symbolic spaces, …