most citedScalable Differentiable Physics for Learning and Control

31 citations · 34 across the 5 of their papers we have counts for

collaborators

6 papers

cs.RO20202 cited

Multi-Agent Coverage in Urban Environments

Shivang Patel, Senthil Hariharan, Pranav Dhulipala +4

We study multi-agent coverage algorithms for autonomous monitoring and patrol in urban environments. We consider scenarios in which a team of flying agents uses downward facing cam…

cs.RO2020

Enhanced Transfer Learning for Autonomous Driving with Systematic Accident Simulation

Shivam Akhauri, Laura Zheng, Ming Lin

Simulation data can be utilized to extend real-world driving data in order to cover edge cases, such as vehicle accidents. The importance of handling edge cases can be observed in…

cs.LG202031 cited

Scalable Differentiable Physics for Learning and Control

Yi-Ling Qiao, Junbang Liang, Vladlen Koltun +1

Differentiable physics is a powerful approach to learning and control problems that involve physical objects and environments. While notable progress has been made, the capabilitie…

cs.CV2019

Shape-Aware Human Pose and Shape Reconstruction Using Multi-View Images

Junbang Liang, Ming C. Lin

We propose a scalable neural network framework to reconstruct the 3D mesh of a human body from multi-view images, in the subspace of the SMPL model. Use of multi-view images can si…

cs.RO20191 cited

ADAPS: Autonomous Driving Via Principled Simulations

Weizi Li, David Wolinski, Ming C. Lin

Autonomous driving has gained significant advancements in recent years. However, obtaining a robust control policy for driving remains challenging as it requires training data from…

cs.RO2019

LSwarm: Efficient Collision Avoidance for Large Swarms with Coverage Constraints in Complex Urban Scenes

Senthil Hariharan Arul, Adarsh Jagan Sathyamoorthy, Shivang Patel +4

In this paper, we address the problem of collision avoidance for a swarm of UAVs used for continuous surveillance of an urban environment. Our method, LSwarm, efficiently avoids co…