15 citations · 40 across the 24 of their papers we have counts for
28 papers
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…
SAD-Flower: Flow Matching for Safe, Admissible, and Dynamically Consistent Planning
Tzu-Yuan Huang, Armin Lederer, Dai-Jie Wu +6
Flow matching (FM) has shown promising results in data-driven planning. However, it inherently lacks formal guarantees for ensuring state and action constraints, whose satisfaction…
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…
Safe Domain Randomization via Uncertainty-Aware Out-of-Distribution Detection and Policy Adaptation
Mohamad H. Danesh, Maxime Wabartha, Stanley Wu +2
Deploying reinforcement learning (RL) policies in real-world involves significant challenges, including distribution shifts, safety concerns, and the impracticality of direct inter…
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…
Improving Generalization in Reinforcement Learning Training Regimes for Social Robot Navigation
Adam Sigal, Hsiu-Chin Lin, AJung Moon
In order for autonomous mobile robots to navigate in human spaces, they must abide by our social norms. Reinforcement learning (RL) has emerged as an effective method to train sequ…
Single-shot Foothold Selection and Constraint Evaluation for Quadruped Locomotion
D. Belter, J. Bednarek, H. -C. Lin +2
In this paper, we propose a method for selecting the optimal footholds for legged systems. The goal of the proposed method is to find the best foothold for the swing leg on a local…
Learning Agile Paths from Optimal Control
Alex Beaudin, Hsiu-Chin Lin
Efficient motion planning algorithms are of central importance for deploying robots in the real world. Unfortunately, these algorithms often drastically reduce the dimensionality o…
Generating Stable and Collision-Free Policies through Lyapunov Function Learning
Alexandre Coulombe, Hsiu-Chin Lin
The need for rapid and reliable robot deployment is on the rise. Imitation Learning (IL) has become popular for producing motion planning policies from a set of demonstrations. How…
Online Dynamic Trajectory Optimization and Control for a Quadruped Robot
Oguzhan Cebe, Carlo Tiseo, Guiyang Xin +3
Legged robot locomotion requires the planning of stable reference trajectories, especially while traversing uneven terrain. The proposed trajectory optimization framework is capabl…
High Precision Real Time Collision Detection
Alexandre Coulombe, Hsiu-Chin Lin
Collision detection and collision avoidance are essential components in these systems for safe human-robot interactions. Robotics systems that can work "out-of-the-box" without exc…
Contact Surface Estimation via Haptic Perception
Hsiu-Chin Lin, Michael Mistry
Legged systems need to optimize contact force in order to maintain contacts. For this, the controller needs to have the knowledge of the surface geometry and how slippery the terra…
Bounded haptic teleoperation of a quadruped robot's foot posture for sensing and manipulation
Guiyang Xin, Joshua Smith, David Rytz +3
This paper presents a control framework to teleoperate a quadruped robot's foot for operator-guided haptic exploration of the environment. Since one leg of a quadruped robot typica…