MSR Speaking Qualifier

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 [...]