15 citations · 40 across the 24 of their papers we have counts for
9 papers · 1 filter
CopyQuad: Quadrupedal Skill Transfer from a Single Side-View Video Without a Depth Model
Mohamad H. Danesh, Raina Hu, Mengxue Xu +1
Video-to-robot pipelines for quadrupedal imitation insert a learned lifting stage, either a monocular depth model or a per-clip 3D reconstruction, to recover the one axis a side-vi…
Imitation Learning via Discrete and Rhythmic Primitive Composition
Rafid Saif, Auke Ijspeert, Hsiu-Chin Lin
Long-horizon robotic manipulation is inherently composed of semantically meaningful sub-tasks. While Movement Primitives (MPs) can model these components, their composition typical…
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…
SafeExplorer: An Unbiased Mixed-Policy Gradient for Reinforcement Learning with Recovery Interventions
Elham Daneshmand, Majid Khadiv, Glen Berseth +1
Training agents directly on the task they will be deployed on often leads to the best policy performance, but training on physical robots can cause damage through falls, so minimiz…
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…
SLowRL: Safe Low-Rank Adaptation Reinforcement Learning for Locomotion
Elham Daneshmand, Shafeef Omar, Glen Berseth +2
Sim-to-real transfer of locomotion policies often leads to performance degradation due to the inevitable sim-to-real gap. Naively fine-tuning these policies directly on hardware is…
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…