PhD Thesis Proposal
Learning to Learn and Structure Learning in Model Spaces for Small Sample Visual Recognition
Yuxiong Wang Carnegie Mellon University Abstract Understanding how to recognize novel categories from few examples for both humans and machines remains a fundamental challenge. Humans are remarkably able to grasp a new category and make meaningful generalization to novel instances from just few examples. By contrast, state-of-the-art machine learning techniques and visual recognition systems typically [...]
Harnessing Task Mechanics for Robotic Manipulation: Modeling, Uncertainty Reduction and Control
Jiaji Zhou Carnegie Mellon University Abstract A high-fidelity and tractable mechanics model of the physical interaction is essential for autonomous robotic manipulation in complex and uncertain environments. Nonetheless, task mechanics are often ignored or nullified in most robotic manipulation systems. This thesis proposal addresses three aspects of harnessing task mechanics: mechanics model learning, uncertainty reduction [...]
Sankalp Arora: Safe, Efficient Data Gathering in Physical Spaces
Sankalp Arora Ph.D. Thesis Proposal Abstract: Reliable and efficient acquisition of data from physical spaces will have countless applications in industry, policy and defense. The capability of gaining information at different scales makes Micro-Aerial Vehicles (MAVs) excellent for aforementioned applications. However, reasoning about information gathering at multiple resolution is NP-Hard and the state of the [...]
Sasanka Nagavalli: Behavior Composition in Human Interaction with Robotic Swarms
Sasanka Nagavalli Ph.D. Thesis Proposal Abstract: Robotic swarms are multi-robot systems whose global behavior emerges from local interactions between individual robots and spatially proximal neighboring robots. Each robot can be programmed with several local control laws that can be activated depending on an operator's choice of global swarm behavior (e.g. flocking, aggregation, formation control, area [...]
Dynamic 3D Reconstruction from the Crowd
Abstract: With the advent of affordable and high-quality smartphone cameras, any significant event, such as a wedding ceremony, a surprised birthday party, or a concert, can be easily captured from multiple of cameras. Automatically organizing such large scale visual data and creating a comprehensive 3D scene model for event browsing is an unsolved problem. State [...]
Measuring and Modeling Kinesic Signals in Social Communication
Abstract: Humans use subtle and elaborate body signals to convey their thoughts, emotions, and intentions. "Kinesics" is a term that refers to the study of such body movements used in social communication, including facial expressions and hand gestures. Understanding kinesic signals is fundamental to understanding human communication; it is among the key technical barriers to [...]
Probabilistic Approaches for Pose Estimation
Abstract: Pose estimation is central to several robotics applications such as registration, manipulation, SLAM, etc. In this thesis, we develop probabilistic approaches for fast and accurate pose estimation. A fundamental contribution of this thesis is formulating pose estimation in a parameter space in which the problem is truly linear and thus globally optimal solutions can [...]
Ada J. Zhang: Personalized Human Motion Classification
Abstract: Algorithms for human motion understanding have a wide variety of applications, including health monitoring, performance assessment, and user interfaces. However, differences between individual styles make it difficult to achieve robust performance, particularly for individuals who were not in the training population. We believe that adapting algorithms to individual behaviors is essential for effective human [...]
Carnegie Mellon University
Learning to learn from simulation: Using simulations to expedite learning on robots
Abstract: Robot controllers, including locomotion controllers, often consist of expert-designed heuristics. These heuristics can be hard to tune, particularly in higher dimensions. It is typical to use simulation to tune or learn these parameters and test on hardware. However, controllers learned in simulation often don't transfer to hardware due to model mismatch. This necessitates controller [...]
Carnegie Mellon University
Visual Learning without Exhaustive Supervision
Abstract Machine learning models have led to remarkable progress in visual recognition. A key driving factor for this progress is the abundance of labeled data. Over the years, researchers have spent a lot of effort curating visual data and carefully labeling it. However, moving forward, it seems impossible to annotate the vast amounts of visual [...]