Carnegie Mellon University
Learning to learn from simulation: Using simulations to learn faster on robots
Abstract: Learning for control is capable of acquiring controllers in novel task scenarios, paving the path to autonomous robots. However, typical learning approaches can be prohibitively expensive in terms of robot experiments, and policies learned in simulation do not transfer directly due to modelling inaccuracies. This encourages learning information from simulation that has a higher [...]
Bipolar Robotics – From the Arctic to the Antarctic with a stop for Fisheries in the middle latitudes.
Abstract: The Arctic, Antarctic and Greenland remain some of the least explored parts of the planet. This talk looks at efforts over the last decade to explore areas under-ice which have traditionally been difficult to access. The focus of the talk will be on the robots, the role of communications over low bandwidth acoustic links, [...]
Video Compression for Recognition & Video Recognition for Compression
Abstract: Training robust deep video representations has proven to be much more challenging than learning deep image representations. One reason is: videos are huge and highly redundant. The 'true' and interesting signal often drowns in too much irrelevant data. In the first part of the talk, I will show how to train a deep network [...]
Multimodal Computational Behavior Understanding
Emotions influence our lives. Observational methods of measuring affective behavior have yielded critical insights, but a persistent barrier to their wide application is that they are labor-intensive to learn and to use. An automated system that can quantify and synthesize human affective behavior in real-world environments would be a transformational tool for research and for [...]
Learning Robot Manipulation Skills through Experience and Generalization
Abstract: In the future, robots could be used to take care of the elderly, perform household chores, and assist in hazardous situations. However, such applications require robots to manipulate objects in unstructured and everyday environments. Hence, in order to perform a wide range of tasks, robots will need to learn manipulation skills that generalize between [...]
Fully Autonomous Drones for Wind Power Turbine Inspection
Abstract: The wind energy industry is growing rapidly. In the U.S. alone, the wind industry invested more than $11 billion in new plants in 2017 and added more than 7,000 megawatts of new capacity, representing 25% of all electric capacity added. One of the biggest challenges to growth remains the high costs of constructing wind [...]
Signal to Symbol (via Skills)
Abstract: While recent years have seen dramatic progress in the development of affordable, general-purpose robot hardware, the capabilities of that hardware far exceed our ability to write software to adequately control. The key challenge here is one of abstraction: generally capable behavior requires high-level reasoning and planning, but perception and actuation must ultimately be performed [...]
Carnegie Mellon University
Sparse and Dense Methods for Underwater Localization and Mapping with Imaging Sonar
Abstract: Imaging sonars have been used for a variety of tasks geared towards increasing autonomy of underwater vehicles: image registration and mosaicing, vehicle localization, object recognition, mapping, and path planning, to name a few. However, the complexity of the image formation has led many algorithms to make the restrictive assumption that the scene geometry is [...]
