Wentao Yuan – MSR Thesis Talk
Newell-Simon Hall 4305Title: 3D Shape Completion and Canonical Pose Estimation with Structured Neural Networks Abstract: 3D point cloud is an efficient and flexible representation of 3D structures and the raw output of many 3D sensors. Recently, neural networks operating on point clouds have shown superior performance on various 3D understanding tasks, thanks to their power to [...]
Planning under Uncertainty with Multiple Heuristics
GHC 6115Abstract: Many robotic tasks, such as mobile manipulation, often require interaction with unstructured environments and are subject to imperfect sensing and actuation. This brings substantial uncertainty into the problems. Reasoning under this uncertainty can provide higher level of robustness but is computationally significantly more challenging. More specifically, sequential decision making under motion and sensing uncertainty [...]
Rawal Khirodkar – MSR Thesis Talk
Newell-Simon Hall 4305Title: Leveraging Simulation for Computer Vision Abstract: A large amount of labeled data is required to train deep neural networks. The process of data annotation on such a large scale is expensive and time-consuming. A promising alternative in this regard is to use simulation to generate labeled synthetic data. However, a network trained solely [...]
Dexterous Manipulation via Simple Robot Hands
GHC 8102Abstract: Most of the industrial robotic applications nowadays can only deal with pick-and-place manipulation, in which fixed graspings are the only interactions between the object and the robot hand. Simple hands, such as pinch grippers and suction cups, suffice to accomplish such tasks. However, there exist many unsolved automation problems where more dexterous manipulations are [...]
Maximilian Sieb – MSR Thesis Talk
Newell-Simon Hall 4305Title: Visual Imitation Learning for Robot Manipulation Abstract: Imitation learning has been successfully applied to solve a variety of tasks in complex domains where an explicit reward function is not available. However, most imitation learning methods require access to the robot's actions during demonstration. This stands in a stark contrast to how we [...]