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 [...]