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Mohamad H Danesh

5 accepted papers

2026

Contractive Diffusion Policies: Robust Action Diffusion via Contractive Score-Based Sampling with Differential Equations

Amin Abyaneh, Charlotte Morissette, Mohamad H. Danesh, Anas Houssaini, David Meger, Gregory Dudek +1
ICLR 2026poster

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 Stochastic Differential Equation (SDE). However, the same score-based SDE modeling that grants diffusion policies the flexib…

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2026

VOCALoco: Viability-Optimized Cost-Aware Adaptive Locomotion

Stanley Wu, Mohamad H. Danesh, Simon Li, Hanna Yurchyk, Amin Abyaneh, Anas El Houssaini +2
RA-L 2026

Recent advancements in legged robot locomotion have facilitated traversal over increasingly complex terrains. Despite this progress, many existing approaches rely on end-to-end deep reinforcement learning (DRL), which poses limitations in terms of safety and interpretability, especially when general

Cited by 1Source ↗Scholar ↗
2024

Contextual Pre-planning on Reward Machine Abstractions for Enhanced Transfer in Deep Reinforcement Learning

Guy Azran, Mohamad H. Danesh, Stefano V. Albrecht, Sarah Keren
AAAI 2024technical

Recent studies show that deep reinforcement learning (DRL) agents tend to overfit to the task on which they were trained and fail to adapt to minor environment changes. To expedite learning when transferring to unseen tasks, we propose a novel approach to representing the current task using reward m…

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2024

Taming the Tail in Class-Conditional GANs: Knowledge Sharing via Unconditional Training at Lower Resolutions

Saeed Khorram, Mingqi Jiang, Mohamad Shahbazi, Mohamad H. Danesh, Li Fuxin
CVPR 2024poster

Despite extensive research on training generative adversarial networks (GANs) with limited training data learning to generate images from long-tailed training distributions remains fairly unexplored. In the presence of imbalanced multi-class training data GANs tend to favor classes with more samples…

Cited by 1Source ↗PDF ↗code ↗Scholar ↗
2021

Re-understanding Finite-State Representations of Recurrent Policy Networks

Mohamad H Danesh, Anurag Koul, Alan Fern, Saeed Khorram
ICML 2021spotlight

We introduce an approach for understanding control policies represented as recurrent neural networks. Recent work has approached this problem by transforming such recurrent policy networks into finite-state machines (FSM) and then analyzing the equivalent minimized FSM. While this led to interesting…

Cited by 29Source ↗PDF ↗code ↗Scholar ↗