10 papers
VIDP: Variable Impedance Diffusion Policy for Compliant Robot Manipulation from Diverse Demonstrations
Hisham Khalil, Neil Fernandes, Thomas M. Kwok +2
Contact-rich manipulation requires precise tracking and mechanical compliance, where variable impedance control can improve robustness in task success, whereas static compliance ca…
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…
SafeExplorer: An Unbiased Policy Gradient for Reinforcement Learning with Recovery Interventions
Elham Daneshmand, Majid Khadiv, Glen Berseth +1
Training reinforcement-learning agents directly on physical robots makes every fall costly, since a fall can damage the platform and cannot be undone like a simulator reset; the go…
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…
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…