Student Talks
Carnegie Mellon University
Planning for a Small Team of Heterogeneous Robots: from Collaborative Exploration to Collaborative Localization
Abstract: Robots have become increasingly adept at performing a wide variety of tasks in the world. However, many of these tasks can benefit tremendously from having more than a single robot simultaneously working on the problem. Multiple robots can aid in a search and rescue mission each scouting a subsection of the entire area in [...]
Carnegie Mellon University
Data Collection for Screwdriving
Abstract: As the use of robotic manipulation in manufacturing continues to increase, the robustness requirements for fastening operations such as screwdriving increase as well. To investigate the reliability of screwdriving and the diverse failure categories that can arise, we collected a dataset of screwdriving operations and manually classified them into stages and result categories. I [...]
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 [...]
Carnegie Mellon University
Learning with Clusters
Abstract As machine learning becomes more ubiquitous, clustering has evolved from primarily a data analysis tool into an integrated component of complex machine learning systems, including those involving dimensionality reduction, anomaly detection, network analysis, image segmentation and classifying groups of data. With this integration into multi-stage systems comes a need to better understand interactions between [...]
Carnegie Mellon University
Intra-Robot Replanning and Learning for Multi-Robot Teams in Complex Dynamic Domains
Abstract: In complex dynamic multi-robot domains, we have a set of individual robots that must coordinate together through a team planner that inevitably makes assumptions based on probabilities about the state of world and the actions of the individuals. Eventually, the individuals may encounter failures, because the team planner’s models of the states and actions [...]
Carnegie Mellon University
Automated Collaborations Among Neighborhood-based Search Heuristics
Abstract: For this thesis, we propose to study how to automatically combine multiple neighborhood-based heuristics. For most computationally challenging problems, there exists multiple heuristics, and it is generally the case that any such heuristic exploits only a limited number of aspects among all the possible problem characteristics that we can think of. As a result, [...]
Carnegie Mellon University
Computational Design Tools for Accessible Robotics
Abstract: A grand vision in robotics is that of a future wherein robots are integrated in daily human life just as smart phones are today. Such pervasive integration of robots would greatly benefit from faster design and manufacturing of robots that cater to individual needs. However, robots of today often take years to be created [...]
Carnegie Mellon University
Predictive Corrective Networks for Action Detection
Abstract: Although computer vision has seen significant advances in static image analysis, the relatively slow advances in video tasks such as action detection suggest we're struggling to build effective temporal models. In this talk, I will present a few main ideas that drive contemporary approaches, such as "two-stream networks" and "3D" convolutional networks. I'll also [...]
Carnegie Mellon University
Soft-Matter Robotic Materials
Abstract: Soft machines and electronics are key components for emerging applications in wearable biomonitoring, human-machine interaction, and soft robotics. In contrast to conventional machines and electronics, soft-matter technologies provide a method for replicating these traditionally rigid devices using intrinsically soft materials that exhibit properties similar to soft biological tissue. This provides a path forward for [...]
Carnegie Mellon University
Exploiting Redundancy for Learning Visual Representations
Abstract: Our visual world is highly structured and the visual data is highly redundant. In recent years, the computer vision field has been transformed by the success of Convolutional Neural Networks (ConvNets). However, the structure and redundancy in visual data has not been well explored in deep learning. The benefits of exploring data redundancy are [...]