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20172026
most citedSingle-shot Foothold Selection and Constraint Evaluation for Quadruped Locomotion

15 citations · 24 across the 19 of their papers we have counts for

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17 papers · 1 filter

cs.RO2026

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…

cs.RO2026

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…

cs.RO2026

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…

cs.RO2026

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…

cs.RO2025

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…

cs.RO2024

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…