42 citations · 338 across the 135 of their papers we have counts for
14 papers · 1 filter
Distributionally Robust and Safe Imitation Learning
Ahmed Aboudonia, Naira Hovakimyan
Imitation learning (IL) has achieved remarkable success in complex decision-making tasks. However, its performance is highly sensitive to distribution shifts, which can pose signif…
Safe Online Learning via Smooth Safety-Structured Policy Composition
Hongpeng Cao, Liqun Zhao, Yuliang Gu +3
Safe online reinforcement learning requires policies to respect safety constraints while maintaining smooth optimization dynamics. Existing approaches typically rely on either stri…
Synergistic Simplex: Cooperative Runtime Assurance for Safety-Critical Autonomous Systems
Ayoosh Bansal, Mikael Yeghiazaryan, Artyom Khachatryan +4
Autonomous systems increasingly rely on machine-learning (ML) components for safety-critical tasks such as perception and control in autonomous vehicles (AVs). While ML enables ess…
Observations Meet Actions: Learning Control-Sufficient Representations for Robust Policy Generalization
Yuliang Gu, Hongpeng Cao, Marco Caccamo +1
Capturing latent variations ("contexts") is key to deploying reinforcement-learning (RL) agents beyond their training regime. We recast context-based RL as a dual inference-control…
Bregman Centroid Guided Cross-Entropy Method
Yuliang Gu, Hongpeng Cao, Marco Caccamo +1
The Cross-Entropy Method (CEM) is a widely adopted trajectory optimizer in model-based reinforcement learning (MBRL), but its unimodal sampling strategy often leads to premature co…
The New Agronomists: Language Models are Experts in Crop Management
Jing Wu, Zhixin Lai, Suiyao Chen +3
Crop management plays a crucial role in determining crop yield, economic profitability, and environmental sustainability. Despite the availability of management guidelines, optimiz…