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Amin Abyaneh

6 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…

Cited by 0Source ↗Scholar ↗
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 ↗
2026

VOCALoco: Viability-Optimized Cost-Aware Adaptive Locomotion

Stanley Wu, Mohamad Hosein Danesh, Simon Li, Hanna Yurchyk, Amin Abyaneh, Anas El Houssaini +2
ICRA 2026poster

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 limi- tations in terms of safety and interpretability, especially when gene…

Cited by 0Source ↗PDF ↗Scholar ↗
2025

Contractive Dynamical Imitation Policies for Efficient Out-of-Sample Recovery

Amin Abyaneh, Mahrokh Ghoddousi Boroujeni, Hsiu-Chin Lin, Giancarlo Ferrari-Trecate
ICLR 2025poster

Imitation learning is a data-driven approach to learning policies from expert behavior, but it is prone to unreliable outcomes in out-of-sample (OOS) regions. While previous research relying on stable dynamical systems guarantees convergence to a desired state, it often overlooks transient behavior.…

Cited by 2Source ↗PDF ↗code ↗Scholar ↗