3 citations · 5 across the 9 of their papers we have counts for
7 papers · 1 filter
KeyGen: Unsupervised Keypoint based Object-Centric Representations for Category-Level Policy Generalization
Shuxin Cao, Liquan Wang, Masoud Moghani +2
Generalization in robotic manipulation requires policies to perform tasks across diverse unseen object instances that vary in shape, size, and pose. However, conventional behavior…
COBALT: Crowdsourcing Robot Learning via Cloud-Based Teleoperation with Smartphones
Ayush Agarwal, Ansh Gandhi, Jeremy A. Collins +6
The scarcity of large-scale, high-quality demonstration data remains a bottleneck in scaling imitation learning for robotic manipulation. We present COBALT, a teleoperation platfor…
Isaac Lab: A GPU-Accelerated Simulation Framework for Multi-Modal Robot Learning
NVIDIA, :, Mayank Mittal +104
We present Isaac Lab, the natural successor to Isaac Gym, which extends the paradigm of GPU-native robotics simulation into the era of large-scale multi-modal learning. Isaac Lab c…
SuFIA-BC: Generating High Quality Demonstration Data for Visuomotor Policy Learning in Surgical Subtasks
Masoud Moghani, Nigel Nelson, Mohamed Ghanem +6
Behavior cloning facilitates the learning of dexterous manipulation skills, yet the complexity of surgical environments, the difficulty and expense of obtaining patient data, and r…
SuFIA: Language-Guided Augmented Dexterity for Robotic Surgical Assistants
Masoud Moghani, Lars Doorenbos, William Chung-Ho Panitch +4
In this work, we present SuFIA, the first framework for natural language-guided augmented dexterity for robotic surgical assistants. SuFIA incorporates the strong reasoning capabil…
ORBIT-Surgical: An Open-Simulation Framework for Learning Surgical Augmented Dexterity
Qinxi Yu, Masoud Moghani, Karthik Dharmarajan +8
Physics-based simulations have accelerated progress in robot learning for driving, manipulation, and locomotion. Yet, a fast, accurate, and robust surgical simulation environment r…
Geometry Matching for Multi-Embodiment Grasping
Maria Attarian, Muhammad Adil Asif, Jingzhou Liu +4
Many existing learning-based grasping approaches concentrate on a single embodiment, provide limited generalization to higher DoF end-effectors and cannot capture a diverse set of…