3 papers
cs.LG2026
Multi-Agent Model-Based Reinforcement Learning with Joint State-Action Learned Embeddings
Zhizun Wang, David Meger
Learning to coordinate many agents in partially observable and highly dynamic environments requires both informative representations and data-efficient training. To address this ch…
cs.LG2026
Contractive Diffusion Policies: Robust Action Diffusion via Contractive Score-Based Sampling with Differential Equations
Amin Abyaneh, Charlotte Morissette, Mohamad H. Danesh +4
Diffusion policies have emerged as powerful generative models for offline policy learning, whose sampling process can be rigorously characterized by a score function guiding a stoc…
cs.RO2025
VOCALoco: Viability-Optimized Cost-aware Adaptive Locomotion
Stanley Wu, Mohamad H. Danesh, Simon Li +5
Recent advancements in legged robot locomotion have facilitated traversal over increasingly complex terrains. Despite this progress, many existing approaches rely on end-to-end dee…