Robot Motion for Seamless Human-Robot Collaboration
Event Location: GHC 8102Abstract: The goal of this thesis is to enable robots to produce motion that is suitable for human-robot collaboration and co-existence. Most motion in robotics is purely functional: industrial robots move to package parts, vacuuming robots move to suck dust, and personal robots move to clean up a dirty table. This type [...]
Interactive Learning for Sequential Decisions and Predictions
Event Location: NSH 1305Abstract: Sequential prediction problems arise commonly in many areas of robotics and information processing: e.g., predicting a sequence of actions over time to achieve a goal in a control task, interpreting an image through a sequence of local image patch classifications, or translating speech to text through an iterative decoding procedure. Learning [...]
Inference Machines: Parsing Scenes via Iterated Predictions
Event Location: NSH 1305Abstract: Extracting a rich representation of an environment from visual sensor readings can benefit many tasks in robotics, e.g., path planning, mapping, and object manipulation. While important progress has been made, it remains a difficult problem to effectively parse entire scenes, i.e., to recognize semantic objects, man-made structures, and landforms. This process [...]
Detecting Object Instances Without Discriminative Features
Event Location: NSH 1305Abstract: In this thesis, we study the topic of detecting object instances which lack discriminative features in scenes with severe clutter and occlusions. Our work focuses on the three key areas: (1) objects that have ambiguous features, (2) objects where discriminative point-based features cannot be reliably extracted, and (3) occlusions. Current approaches [...]
Dense non-rigid motion capture from monocular video
Event Location: NSH 1507Bio: Ravi Garv is a final year PhD student at the School of Electronic Engineering and Computer Science, at Queen Mary University of London, working under the supervision of Dr. Lourdes Agapito, who holds an ERC Starting Grant. His work focuses on dense reconstruction of non-rigid surfaces and dynamic scenes. Mr. Garv [...]
Jointly Aligning and Segmenting Multiple Web Photo Streams for the Inference of Collective Photo Storylines
Event Location: NSH 1507Bio: Gunhee Kim is a PhD candidate advised by Eric P. Xing at Computer Science Department of Carnegie Mellon University. Prior to starting PhD study in 2009, he earned a master’s degree under supervision of Martial Hebert in Robotics Institute, CMU. He also worked as a visiting student in Antonio Torralba's group [...]
Figure-Ground Segmentation and Pooling for Visual Scene Analysis
Bio: Cristian Sminchisescu is a Professor in the Department of Mathematics, Faculty of Engineering, at Lund University. He has obtained a doctorate in computer science and applied mathematics with specialization in imagining, vision and robotics at INRIA, France, under an Eiffel excellence doctoral fellowship, and has done postdoctoral research in the Artificial intelligence Laboratory at [...]
Obstacle Avoidance and Targeted Flight for Rotary Wing MAVs in GPS-Denied Environments
Event Location: GHC 2109Bio: Chen (pronounced “Ken”) is a PhD Candidate at the University of Maryland, College Park. His research focuses on SLAM for micro aerial vehicles. Previously, he worked as an engineer for the Israel CFD center and in Helicopter Simulations at the Aerospace Engineering Department at Technion, Israel. He received his BS and [...]
Special Joint RI/VASC Seminar
Event Location: NSH 1305Bio: Shoji TOMINAGA is a professor at Graduate School of Advanced Integration Science, Chiba University. His research interests include color imaging, illuminant estimation, multispectral image analysis, and color image rendering. He is an editorial board member of Color Research and Application, an associate editor of the Journal of Electronic Imaging, an executive [...]
Physics-Based Manipulation Planning in Cluttered Human Environments
Event Location: NSH 1305Abstract: This thesis presents a series of planners and algorithms for manipulation in cluttered human environments. The focus is on using physics-based predictions, particularly for pushing operations, as an effective way to address the manipulation challenges posed by these environments. We introduce push-grasping, a physics-based action to grasp an object first by pushing it and [...]