Student Talks
Carnegie Mellon University
Nikhil Jog – MSR Thesis Talk
Title: Highly Miniaturized Robots for Inspection of Small Nuclear Piping Abstract: Bomb making in the 20th century resulted in the creation of massive facilities to produce Uranium. As part of a multi-billion-dollar agenda, the measurement of radioactivity is required for the safe disposal of residual Uranium in piping. Manual techniques have proven too approximate, [...]
Carnegie Mellon University
Contrastive View Predictive Learning with 3D-Bottlenecked RNNs
Abstract: In this talk, I will describe our recent work on neural architectures for visual recognition, which use 3D not as input nor as the desired output space, but rather as the bottleneck of the learned representations. We consider embodied agents moving in otherwise static worlds equipped with these architectures; they learn 3D visual feature [...]
Chao Cao – MSR Thesis Talk
Title: Topological Path Planning for Mobile Robot Applications Abstract: Many path planning problems in mobile robot applications can be solved more efficiently in the topological space. By using the language of topology, the richer spatial information failed to captured by graph/grid-based map representations can be explicitly expressed and exploited. With that, it is possible [...]
Carnegie Mellon University
MSR Thesis Talk – Tao Chen
Title: Deep Reinforcement Learning with Prior Knowledge Abstract: Deep reinforcement learning has been applied to many domains from computer games, natural language processing, recommendation systems to robotics. While model-free reinforcement learning algorithms are promising approaches to learning policies without knowledge of the system dynamics, they usually require much more data. In this thesis, we [...]
Carnegie Mellon University
Towards Understanding and Mitigating Biases
Abstract: There are many problems in real life that involve collecting and aggregating evaluation from people, such as conference peer review and peer grading. In this thesis, we consider multiple sources of biases that may arise in this process: (1) human bias -- the data collected from people are noisy and reflect people's calibration criteria [...]
Toward Intent Recognition through Nonverbal Behaviors in Assistive Co-Manipulation
Abstract: Robots are becoming more versatile, increasing the available opportunities to use them in situations that aid people in everyday tasks. For example, recent research has investigated robot manipulators for assisting people with motor impairments in activities of daily living such as eating a meal. To form successful collaborations in these interactions, researchers need to [...]
Carnegie Mellon University
Anqi Yang – MSR Thesis Talk
Title: 3D Object Detection from CT Scans using a Slice-and-fuse Approach Abstract: Automatic object detection in 3D X-ray Computed Tomography imagery has recently gained research attention due to its promising applications in aviation baggage screening. The huge dimension of an individual 3D scan, however, poses formidable computational challenges when coupled with deep 3D convolutional [...]
Carnegie Mellon University
Tian Ye – MSR Thesis Talk
Title: Interpretable Intuitive Physics Model Abstract: Humans have a remarkable ability to use physical commonsense and predict the effect of collisions. But do they understand the underlying factors? Can they predict if the underlying factors have changed? Interestingly, in most cases humans can predict the effects of similar collisions with different conditions such as changes [...]
Carnegie Mellon University
Rotational Distributions for Pose Estimation
Abstract: For robots to operate robustly in the real world, they should be aware of their uncertainty, particularly when estimating the position and orientation, or pose, of objects. This uncertainty can be caused by many factors, such as occlusions, poor lighting, or object symmetry. These factors can naturally induce an inherent ambiguity in terms of [...]
Carnegie Mellon University
Hunter Goforth – MSR Thesis Talk
Title: Learning for Registration in 2D and 3D Abstract: We explore the application of deep learning to 2D (image) and 3D (point cloud) registration, especially in scenarios where traditional methods can fail. In the 2D case, we apply a recently-proposed learning method to the problem of aligning outdoor imagery taken across different seasons or [...]