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GAMMS: Graph based Adversarial Multiagent Modeling Simulator
As intelligent systems and multi-agent coordination become increasingly central to real-world applications, there is a growing need for …
Rohan Patil, Jai Malegaonkar, Xiao Jiang, Andre Dion, Gaurav S Sukhatme, Henrik I Christensen
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GAMMS: Graph based Adversarial Multiagent Modeling Simulator
Squint: Fast Visual Reinforcement Learning for Sim-to-Real Robotics
Visual reinforcement learning is appealing for robotics but expensive – off-policy methods are sample-efficient yet slow; …
Abdulaziz Almuzairee, Henrik I. Christensen
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Squint: Fast Visual Reinforcement Learning for Sim-to-Real Robotics
BridgeSim: Unveiling the OL-CL Gap in End-to-End Autonomous Driving
Open-loop (OL) to closed-loop (CL) gap (OL-CL gap) exists when OL-pretrained policies scoring high in OL evaluations fail to transfer …
Seth Z. Zhao, Luobin Wang, Hongwei Ruan, Yuxin Bao, Yilan Chen, Ziyang Leng, Abhijit Ravichandran, Honglin He, Zewei Zhou, Xu Han, Abhishek Peri, Zhiyu Huang, Pranav Desai, Henrik Christensen, Jiaqi Ma, Bolei Zhou
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BridgeSim: Unveiling the OL-CL Gap in End-to-End Autonomous Driving
ROSCOE: Robot Scanning and Computing Equipment for Autonomous Terrestrial Mapping
Autonomous task-oriented robots are increasingly in demand across various domains; however, few existing systems address the challenge …
Julian Raheema, Seth Farrell, Michael Hess, Raymond Provost, Mark Bilinski, Henrik I Christensen
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ROSCOE: Robot Scanning and Computing Equipment for Autonomous Terrestrial Mapping
Safe Human Robot Navigation in Warehouse Scenario
The integration of autonomous mobile robots (AMRs) in industrial environments, particularly warehouses, has revolutionized logistics …
Seth Farrell, Chenghao Li, Hongzhan Yu, Ryo Yoshimitsu, Sicun Gao, Henrik I. Christensen
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Safe Human Robot Navigation in Warehouse Scenario
Merging and Disentangling Views in Visual Reinforcement Learning for Robotic Manipulation
Vision is well-known for its use in manipulation, especially using visual servoing. To make it robust, multiple cameras are needed to …
Abdulaziz Almuzairee, Rohan Prashant Patil, Dwait Bhatt, Henrik I Christensen
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Merging and Disentangling Views in Visual Reinforcement Learning for Robotic Manipulation
Towards Embodiment Scaling Laws in Robot Locomotion
Developing generalist agents that operate across diverse tasks, environments, and robot embodiments is a grand challenge in robotics …
Bo Ai, Liu Dai, Nico Bohlinger, Dichen Li, Tongzhou Mu, Zhanxin Wu, K. Fay, Henrik I Christensen, Jan Peters, Hao Su
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Towards Embodiment Scaling Laws in Robot Locomotion
cpRRTC: GPU-Parallel RRT-Connect for Constrained Motion Planning
Motion planning is a fundamental problem in robotics that involves generating feasible trajectories for a robot to follow. Recent …
Jiaming Hu, Jiawei Wang, Henrik Christensen
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cpRRTC: GPU-Parallel RRT-Connect for Constrained Motion Planning
Learning Dexterous Deformable Object Manipulation Through Cross-Embodiment Dynamic Learning
Dexterous manipulation of deformable objects remains a core challenge in robotics due to complex contact dynamics and high-dimensional …
Zihao He, Bo Ai, Yulin Liu, Weikang Wan, Henrik I Christensen, Hao Su
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Learning Dexterous Deformable Object Manipulation Through Cross-Embodiment Dynamic Learning
Using Language and Road Manuals to Inform Map Reconstruction for Autonomous Driving
Lane-topology prediction is a critical component of safe and reliable autonomous navigation. An accurate understanding of the road …
Akshar Tumu, Henrik I. Christensen, Marcell Vazquez-Chanlatte, Chikao Tsuchiya, Dhaval Bhanderi
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Using Language and Road Manuals to Inform Map Reconstruction for Autonomous Driving