Date: Tuesday, July 19, 2022 Time: 9:00 AM - 10:00 AM ET Location: Newell-Simon Hall (NSH) 3305 Title: Detect Active Object in a Sequential Voting Process Abstract: A key component of understanding hand-object interactions is the ability to identify the active object -- the object that is being manipulated by the human hand. In order to accurately localize the [...]

Title: Real-Time Visual Localization System in Changing and Challenging Environments via Visual Place Recognition   Abstract: Localization is one of the fundamental capabilities to guarantee reliable robot autonomy. Many excellent Visual-Inertial and LiDAR-based algorithms have been developed to solve the localization problem. However, deploying these methods on a real-time portable device is challenging due to high [...]

Title: Seeing the Unseen: Closed-loop Occlusion Reasoning for Cloth Manipulation Robotic manipulation of cloth remains challenging due to the complex dynamics of cloth, lack of a low-dimensional state representation, and self-occlusions. Particularly, self-occlusion makes it difficult to estimate the full state of the cloth, which poses significant challenges to policy learning and dynamics modeling. Ideally, [...]

Title: Features in Extra Dimensions: Spatial and Temporal Scene Representations Abstract: Computer vision models have made great progress in featurizing pixels of images. However, an image is only a projection of the actual 3D scene: occlusions and perspective distortions exist. To arrive at a better representation of the scene itself, extra dimensions are needed to [...]

Title: Library of behaviors and tools for robot manipulation Abstract: Learned policies often fail to generalize across environment variations, such as, different objects, object arrangements, or camera viewpoints. Moreover, most policies are trained and tested in simulation environments, and the sim2real gap remains large under weak visual representations that do not disentangle the scene from [...]

Title: 6D Object Pose Estimation for Manipulation via Weak Supervision Abstract: 6D object pose estimation is essential for robotic manipulation tasks. Existing learning-based pose estimators often rely on training from labeled absolute poses with fixed object canonical frames, which (1) requires datasets with annotations of object absolute pose that are resource-intensive to collect; (2) is hard [...]

Title: Using 3D Imaging Radar for Indoor Localization and Mapping Zoom: https://cmu.zoom.us/j/95090884062?pwd=dVZDVHJDTGVUWW9iSlJLTWtidThBUT09 Meeting ID: 950 9088 4062 Passcode: 411959 Abstract: 3D Imaging Radars offer robust perception capability through visually demanding environments due to the unique penetrative and reflective properties of millimeter waves. However, the utilization of Imaging radar for robot navigation and mapping remains under-explored due [...]

Title: Programmable light curtains for Safety Envelopes, SLAM and Navigation Abstract: Conventional robot perception and navigation pipelines are built using traditional sensors such as RGB cameras, stereo depth sensors and LiDARs.These sensors scan the entire scene in a fixed and uniform way. In contrast, programmable light curtains are a recently-invented, resource-efficient sensor that measure the [...]

Title: Lidar-Visual-Inertial Odometry via Modifications and Improvements to Super Odometry Abstract:     The main focus of this thesis involves improvements and extensions to Super Odometry, a preexisting method for lidar-inertial odometry. This was done in the context of the DARPA RACER program as a member of Carnegie Mellon's DEAD Fast team, aiming to provide reliable [...]

Title: Boundary-Aware Demons Algorithm with Applications in Electronic Waste Recycling Abstract Electronic waste (e-waste) refers to electronic devices that are nearing the end of their useful life, and are discarded, donated, or given away. Valuable metallic and plastic components in e-waste (gold, silver, platinum) is estimated to value upwards of $60 billion and although e-waste represents [...]

Title: Introspective Perception through Identifying Blur, Light Direction, and Angle-of-View Abstract Robotic perception tasks have achieved great performance, especially in autonomous vehicles and robot assistance. However, we still often do not understand how and when perception tasks fail. Researchers have achieved some success in creating introspective perception systems that detect when perception tasks will fail, [...]