MSR Speaking Qualifier

Abstract: Detect-and-Avoid (DAA) capabilities are critical for autonomous operations of small unmanned aircraft systems (sUAS). Traditionally DAA systems for large aircraft have been ground and radar-based. Due to the size, weight, and power (SWaP) constraints of sUAS, current DAA systems rely mainly on vision-based sensors and ADS-B (Automatic Dependent Surveillance-Broadcast) transponders. However, not all flying [...]

Title: Preprocessing-based Methods for Robotic Manipulation Abstract: Robotic manipulation is a key problem for several applications such as welding, pick-and-place, and automated assembly. However, motion planning for manipulation can be computationally expensive as it requires planning in the high-dimensional configuration space of the manipulator. Additionally, task-specific constraints such as strict time limits or constraints on end-effector [...]

Title: Socially-Aware Trajectory Prediction Guided by Motion Patterns Abstract: As intelligent robots across domains start collaborating with humans in shared environments, e.g., urban settings and terminal airspace, algorithms that enable them to reason over human motion and intent are important to enable seamless and safe interplay. In our work, we study human intent by focusing on the [...]

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, [...]

Title: Image Synthesis with Appearance Decomposition Abstract: Our visual world is compositional and its appearance can be decomposed into various components. Leveraging these components can be beneficial for challenging image synthesis tasks. To this end, this thesis focuses on studying how appearance decomposition can improve image synthesis methods using two examples. (1) Structural decomposition: we introduce [...]

Title: A VPR-Based Technique for UAV Localization In Unseen Environments Abstract: Unmanned Aerial Vehicles (UAVs) primarily rely on GPS-assisted localization and navigation due to the accessibility and ubiquity of such systems. However, this presents a potentially catastrophic single point of failure that may prevent autonomous UAVs from becoming truly reliable, as GPS is prone to dropout, [...]

Title: GPS-Denied Global Visual-Inertial Ground Vehicle State Estimation via Image Registration Abstract: Robotic systems such as unmanned ground vehicles (UGVs) often depend on GPS for navigation in outdoor environments. In GPS-denied environments, one approach to maintain a global state estimate is localizing based on preexisting georeferenced aerial or satellite imagery. However, this is inherently challenged [...]

Title: Learning to Distill Datasets by Matching Expert Training Trajectories Project Page: https://georgecazenavette.github.io/mtt-distillation/ Abstract: Dataset distillation is the task of synthesizing a small dataset such that a model trained on the synthetic set will match the test accuracy of the model trained on the full dataset. In this talk, we review 3 several of our recent [...]

Title: Coordinating Heterogeneous Teams for Urban Search and Rescue Abstract: The mission of Urban Search and Rescue (USAR) has drawn significant interest in robotics. Autonomous entities must be able to share knowledge efficiently to address visibility and collaboration challenges in a complex environment shortly after structural collapse catastrophes. In this thesis, we present methods to coordinate [...]

Title: Composition Learning in “Modular” Robot Systems Abstract: Modular robot and multi-robot systems share a concept in common: composition,  i.e. the study of how parts can be combined so they can be used to achieve certain objectives. Our vision is to enable robotic systems to configure and reconfigure themselves during field deployment, either autonomously or [...]

Title: Towards HD Map Updates With Crosswalk Change Detection From Vehicle-mounted Cameras Zoom: https://cmu.zoom.us/j/4452379705   Abstract: Many autonomous vehicles rely on high-definition maps that contain road layout and road semantics as priors for perception, planning and prediction. However, these maps can become stale over time as the road environment changes. This thesis develops a road monitoring framework [...]

Title: Taxim: An Example-based Simulation Model for GelSight Tactile Sensors and its Sim-to-Real Applications Location: NSH 4305 or Zoom https://cmu.zoom.us/j/91769761787?pwd=cGZ2RElKMVJaQ1NVNG5BdFQ0Ny9uQT09 Abstract: Simulation is widely used in robotics for system verification and large-scale data collection. However, simulating a robot system efficiently and with high fidelity, from sensing, perception to manipulation, has been a long-standing challenge. Tactile sensing, as [...]

Title: A Low-Cost Attitude Determination and Control System and Hardware-in-the-Loop Testbed for CubeSats Zoom: https://cmu.zoom.us/j/92654622790?pwd=d0pYcTJ4K0xzdmYvUHFYWC9lMDBhQT09   Abstract: Since their initial development in the late 1990s, CubeSats have quickly grown popular due to their relatively low cost and short development period. However, CubeSat launches are prone to failure, with less than half of CubeSats completely fulfilling their [...]

Title: Driving by Dreaming: Offline Model-Based Reinforcement Learning for Motion Planning for Autonomous Vehicles Abstract: While there has been significant progress in deploying autonomous vehicles (AVs) in urban driving settings, there remains a long-tail of challenging motion planning scenarios that must be addressed before truly driverless operation is possible. The current paradigm for motion planner [...]

Title: Learning from Physical Human Feedback: An Object-Centric One-Shot Adaptation Method Abstract: For robots to be effectively deployed in novel environments and tasks, they must be able to understand the feedback expressed by humans during intervention. This can either correct undesirable behavior or indicate additional preferences. Existing methods either require repeated episodes of interactions or [...]

Title: Dense Human Pose Estimation From WiFi Abstract: Advances in computer vision and machine learning techniques have led to significant development in 2D and 3D human pose estimation from RGB cameras, LiDAR, and radars. However, human pose estimation from images is adversely affected by occlusion and lighting, which are common in many scenarios of interest. [...]

Title: An efficient approach for sequential shape human performance capture from monocular video Abstract: Human performance capture from RGB videos in unconstrained environments has become very popular for applications to generate virtual avatars or digital actors. Modern approaches rely on neural network algorithms to estimate geometry directly from images, resulting in a coarse representation of [...]

Title: Efficient Methods for Model Performance Inference Abstract: A key challenge in neural architecture search (NAS) is quickly inferring the predictive performance of a broad spectrum of neural networks to discover statistically accurate and computationally efficient ones. We refer to this task as model performance inference (MPI). The current practice for efficient MPI is gradient-based methods [...]

Title: Addressing Time-series Signal Quality in Healthcare Data Abstract: Healthcare data time-series signal quality assessment (SQA) plays a vital role in the accuracy and reliability of machine learning algorithms to analyze health metrics. However, these signals are often corrupted with different kinds of noises and artifacts, including Baseline Wander, Muscle Artifacts, Powerline Interference, and Equipment Failure. This [...]

Title: Search-based Planning for Sensor-based Coverage Abstract: Robots are excellent candidates for the dull, dirty, and dangerous jobs we do not want humans to perform. Today, these include inspection of large areas or structures, post-disaster assessment, and surveillance. Assessing the aftermath of the recent Fern Hollow bridge collapse in Pittsburgh is one such example. Many [...]

Title: See But Don't Be Seen: Towards Stealthy Active Search in Heterogeneous Multi-Robot Systems Abstract: Robotic solutions for quick disaster response are essential to ensure minimal loss of life, especially when the search area is too dangerous or too vast for human rescuers. We model this problem as an asynchronous multi-agent active-search task where each robot aims [...]

Title: Spatial Tasking in Human-Robot Collaborative Exploration Abstract: This work develops a methodology for collaborative human-robot exploration that leverages implicit coordination. Most autonomous single- and multi-robot exploration systems require a remote operator to provide explicit guidance to the robot team. Few works consider how to integrate the human partner alongside robots to provide guidance in the [...]

Title: Towards Video-based Physiology Estimation Abstract: RGB-video based human physiology estimation has a wide range of practical applications in telehealth, sports and deep fake detection. Therefore, researchers in the community have collected several video datasets and have advanced new methods over the years. In this dissertation, we study these methods extensively and aim to address the [...]

Title: Automating Ultrasound Based Vascular Access Abstract: Timely care of trauma patients is important to prevent casualties in resource-limited regions such as the battlefield. In order to treat such trauma using point of care diagnosis, medical practitioners typically use an ultrasound for vascular access or detection of subcutaneous splinters for providing critical care. The problem here is two-fold: [...]

Title: Analogical Networks: Memory-Modulated In-Context 3D Parsing Abstract: Recent advances in the applications of deep neural networks to numerous visual perception tasks have shown excellent performance. However, this generally requires access to large amount of training samples and hence one persistent challenge is the setting of few-shot learning. In most existing works, a separate parametric neural [...]

Title: ARC: AdveRsarial Calibration between Modalities Abstract: Advances in computer vision and machine learning techniques have led to flourishing success in RGB-input perception tasks, which has also opened unbounded possibilities for non-RGB-input perception tasks, such as object detection from wireless signals, point clouds, and infrared light. However, compared to the matured development pipeline of RGB-input [...]

Title: Data-Driven Slip Model for Improved Localization and Path Following applied to Lunar Micro-Rovers Abstract Micro-lunar rovers need to solve a slew of challenges on the Moon, with no human intervention. One such challenge is the need to know their location in order to navigate and build maps. However, localization is challenging on the moon due [...]