3 citations · 4 across the 8 of their papers we have counts for
9 papers
Drift Q-Learning
Anas Houssaini, Mohamad H. Danesh, Amin Abyaneh +3
Offline reinforcement learning requires improving a policy from fixed data while avoiding out-of-distribution actions with unreliable value estimates. Diffusion and flow policies h…
Morphology-Conditioned World Model for Cross-Embodiment Quadrupedal Locomotion
Mohamad H. Danesh, Chenhao Li, Amin Abyaneh +5
World models promise a paradigm shift in robotics, where an agent learns the physics of its environment once and then acquires behaviors efficiently. Yet the learned dynamics model…
Tactile Modality Fusion for Vision-Language-Action Models
Charlotte Morissette, Amin Abyaneh, Wei-Di Chang +5
We propose TacFiLM, a lightweight modality-fusion approach that integrates visual-tactile signals into vision-language-action (VLA) models. While advances in VLAs have introduced r…
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…
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…
Contractive Dynamical Imitation Policies for Efficient Out-of-Sample Recovery
Amin Abyaneh, Mahrokh G. Boroujeni, Hsiu-Chin Lin +1
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 resear…
Single-Shot Learning of Stable Dynamical Systems for Long-Horizon Manipulation Tasks
Alexandre St-Aubin, Amin Abyaneh, Hsiu-Chin Lin
Mastering complex sequential tasks continues to pose a significant challenge in robotics. While there has been progress in learning long-horizon manipulation tasks, most existing a…
Globally Stable Neural Imitation Policies
Amin Abyaneh, Mariana Sosa Guzmán, Hsiu-Chin Lin
Imitation learning presents an effective approach to alleviate the resource-intensive and time-consuming nature of policy learning from scratch in the solution space. Even though t…
Learning Lyapunov-Stable Polynomial Dynamical Systems through Imitation
Amin Abyaneh, Hsiu-Chin Lin
Imitation learning is a paradigm to address complex motion planning problems by learning a policy to imitate an expert's behavior. However, relying solely on the expert's data migh…