Calendar of Events
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1 event,
PhD Thesis Proposal
Kumar Shaurya Shankar
MRFMaps: A Representation for Multi-Hypothesis Dense Volumetric SLAM
Abstract: Robust robotic flight requires tightly coupled perception and control. Conventional approaches employ a SLAM algorithm to infer the most likely trajectory and then generate an occupancy grid map using dense sensor data for planning purposes. In such approaches all the robustness and accuracy costs are offset to the SLAM algorithm; if there are any […]
2 events,
PhD Thesis Defense
Akshara Rai
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 [...]
RI Seminar
Hanumant Singh
Mechanical & industrial Engineering, Northeastern University
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, [...]
1 event,
VASC Seminar
Philipp Krähenbühl
Computer Science Department, University of Texas at Austin
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 [...]
1 event,
Faculty Candidate
Laszlo A. Jeni
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 [...]
1 event,
RI Seminar
Oliver Kroemer
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 [...]
2 events,
Special Talk
Dr. Yan Ke
Colbotics
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 [...]
RI Seminar
George Konidaris
Department of Computer Science, Brown University
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 [...]
1 event,
PhD Thesis Proposal
Eric Westman
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 [...]
1 event,
PhD Thesis Proposal
Chen Kong
Deep Interpretable Non-rigid Structure from Motion
Abstract: Current non-rigid structure from motion (NRSfM) algorithms are limited with respect to: (i) the number of images, and (ii) the type of shape variability they can handle. This has hampered the practical utility of NRSfM for many applications within vision. Deep Neural Networks (DNNs) are an obvious candidate to help with such issue. However, […]
1 event,
Faculty Events
RI Faculty Social
NSH 4305, 3-4 PM
RI Faculty Social All Robotics Institute faculty are invited to attend this informal team-building business/social event
All Robotics Institute faculty are invited to attend this informal team-building business/social event. Our November Robotics Institute Faculty Social will be hosted by Martial Hebert in NSH 4305, from 3:00 to 4:00pm.
1 event,
Field Robotics Center Seminar
Rogerio Bonatti
Autonomous drone cinematographer: Using artistic principles to create smooth, safe, occlusion-free trajectories for aerial filming
Abstract: Autonomous aerial cinematography has the potential to enable automatic capture of aesthetically pleasing videos without requiring human intervention, empowering individuals with the capability of high-end film studios. Current approaches either only handle off-line trajectory generation, or offer strategies that reason over short time horizons and simplistic representations for obstacles, which result in jerky movement and […]
5 events,
PhD Speaking Qualifier
Anahita Mohseni Kabir
Robot Task Execution by Policy Adaptation and Switching Among Multiple Tasks
Abstract: While mobile robots reliably perform service tasks by accurately localizing and safely navigating while avoiding obstacles, they do not respond in any other way to their surroundings. In this work, we introduce two methods that enable the robots to be more responsive to their environment, including humans and other robots. The first algorithm enables […]
Field Robotics Center Seminar
Visual SLAM with Semantic Scene understanding
Visual SLAM with Semantic Scene understanding
Abstract: Simultaneous localization and mapping (SLAM) has been widely used in autonomous robots and virtual reality. It estimates the sensor motion and maps the environment at the same time. The classic sparse feature point map of visual SLAM is limited for many advanced tasks including robot navigation and interactions, which usually require a high-level understanding of […]
PhD Thesis Proposal
Chao Liu
Vision with Small Baselines
Abstract: Portable camera sensor systems are becoming more and more popular in computer vision applications such as autonomous driving, virtual reality, robotics manipulation and surveillance, due to the decreasing expense and size of RGB camera. Despite the compactness and portability of the small baseline vision systems, it is well-known that the uncertainty in range finding […]
RI Seminar
Peter K. Allen
Department of Computer Science, Columbia University
Multi-Modal Geometric Learning for Grasping
Abstract: In this talk, we will describe methods to enable robots to grasp novel objects using multi-modal data and machine. The starting point is an architecture to enable robotic grasp planning via shape completion using a single occluded depth view of objects. Shape completion is accomplished through the use of a 3D CNN. The network […]
PhD Thesis Proposal
Aayush Bansal
Machine Imagination: Data-driven User Controllable Visual Content Creation
Abstract: Humans have the remarkable ability to create visual worlds far beyond what could be seen by human eye, including inferring the state of unobserved, imagining the unknown, and thinking about diverse possibilities about what lies in the future. Machines lack this inquisitive ability despite the current revolution in machine learning and computer vision. We […]
Carnegie Mellon University
Carnegie Mellon University
Carnegie Mellon University
Carnegie Mellon University
Carnegie Mellon University
Carnegie Mellon University
Carnegie Mellon University
Carnegie Mellon University
Carnegie Mellon University