Min Liu, Zherong Pan, Kai Xu, and Dinesh Manocha
Classical robotics research presents the first grasp planning algorithm to compute globally optimal gripper pose that maximizes a grasp metric.
Senthil Hariharan Arul, Dinesh Manocha
A new algorithm for decentralized collision avoidance for quadrotor swarm navigation in dense environments with static and dynamic obstacles.
Zhe Hu, Tao Han, Peigen Sun, Jia Pan, Dinesh Manocha
Deep neural network-based controller to servo-control position and shape of deformable objects with unknown deformation properties.
IEEE Robotics and Automation Letters
Qingyang Tan, Tingxiang Fan, Jia Pan, Dinesh Manocha
A novel algorithm (DeepMNavigate) for global multi-agent navigation in dense scenarios using deep reinforcement learning. | Watch a video about DeepMNavigate |
Rohan Chandra, Uttaran Bhattacharya, Trisha Mittal, Xiaoyu Li, Aniket Bera, Dinesh Manocha
The GraphRQI algorithm identifies driver behaviors from road agent trajectories. It is 25 percent more accurate over prior behavior classification algorithms for autonomous vehicles. | Watch a video about GraphRQI |
Qingyang Tan, Zherong Pan, Lin Gao, and Dinesh Manocha
A new method bridges the gap between mesh embedding and physical simulation for efficient dynamic models of clothes. The key technique is a graph-based convolutional neural network (CNN) defined on meshes with arbitrary topologies, and a new mesh embedding approach based on physics-inspired loss term. After training, the learned simulator runs10–100 times faster and the accuracy is high enough for robot manipulation tasks. | Watch a video about this method |