Student Talks
Discovering and Leveraging Visual Structure for Large-scale Recognition
Abstract: Our visual world is extraordinarily varied and complex, but despite its richness, the space of visual data may not be that astronomically large. We live in a well-structured, predictable world, where cars almost always drive on roads, sky is always above the ground, and so on. As humans, the ability to learn this structure [...]
Deliberative Perception
Abstract: A recurrent and elementary robot perception task is to identify and localize objects of interest in the physical world. In many real-world situations such as in automated warehouses and assembly lines, this task entails localizing specific object instances with known 3D models. Most modern-day methods for the 3D multi-object localization task employ scene-to-model feature [...]
Carnegie Mellon University
Compact Generative Models of Point Cloud Data for 3D Perception
Abstract: One of the most fundamental tasks for any robotics application is the ability to adequately assimilate and respond to incoming sensor data. In the case of 3D range sensing, modern-day sensors generate massive quantities of point cloud data that strain available computational resources. Dealing with large quantities of unevenly sampled 3D point data is [...]
Carnegie Mellon University
Mathematical Models of Adaptation in Human-Robot Collaboration
Abstract: While much work in human-robot interaction has focused on leader- follower teamwork models, the recent advancement of robotic systems that have access to vast amounts of information suggests the need for robots that take into account the quality of the human decision making and actively guide people towards better ways of doing their task. [...]
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
Training Strategies for Time Series: Learning for Prediction, Filtering, and Reinforcement Learning
Abstract: Data driven approaches to modeling time-series are important in a variety of applications from market prediction in economics to the simulation of robotic systems. However, traditional supervised machine learning techniques designed for i.i.d. data often perform poorly on these sequential problems. This thesis proposes that time series and sequential prediction, whether for forecasting, filtering, [...]
Carnegie Mellon University
Expressive Real-time Intersection Scheduling
Abstract: Traffic congestion is a major annoyance throughout global metropolitan areas. This talk will present Expressive Real-time Intersection Scheduling (ERIS), a schedule-driven control strategy for adaptive intersection control to reduce traffic congestion. ERIS maintains separate estimates for each lane approaching a traffic intersection allowing it to more accurately estimate the effects of scheduling decisions than [...]
Carnegie Mellon University
Scaling up Self Supervised Robot Learning
Abstract Robot learning holds promise in alleviating several real world problems, by performing complex behaviors in complex environments. But what is the right way to train these robots? Our methods on self supervision shows encouraging results on several tasks like grasping objects, pushing objects and even flying drones. One key challenge with these methods is [...]
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 [...]