works on

From the 1 of 8 linked papers with an AI index.

collaborators

8 papers

cs.RO2026

Tactile Modality Fusion for Vision-Language-Action Models

Charlotte Morissette, Amin Abyaneh, Wei-Di Chang +5

The paper introduces TacFiLM, a lightweight method that fuses tactile data with visual features in vision‑language‑action models to improve robot manipulation tasks that involve co…

cs.LG2026

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…

cs.SE2026

CA2: Code-Aware Agent for Automated Game Testing

Valliappan Chidambaram Adaikkappan, Vincent Martineau, Joshua Romoff +1

Automated game testing is important for verifying game functionality, but it remains a costly and time-consuming process. Manual testing often misses edge cases, and current automa…

cs.LG2026

Multi-scale Predictive Representations for Goal-conditioned Reinforcement Learning

Valliappan Chidambaram Adaikkappan, David Meger, Sai Rajeswar +1

This paper investigates robust representation learning in offline goal-conditioned reinforcement learning (GCRL). Particularly in sparse reward scenarios, learning representations…

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.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…