PhD Speaking Qualifier
PhD Student
Robotics Institute,
Carnegie Mellon University

Learning to perform dynamic and interactive tasks using structural and algorithmic priors

NSH 3002

Abstract: Everyday human tasks such as picking up an object in one smooth motion, pushing a heavy door using the momentum of our bodies or pushing off a wall to quickly turn a corner involve complex dynamic interactions between the human and the environment, as well as switching dynamics when the robot makes and breaks [...]

PhD Speaking Qualifier
PhD Student
Robotics Institute,
Carnegie Mellon University

Simple Shape Descriptors for Retinal Surface Estimation using a Laser-Aiming Beam

Abstract: Retinal surgery procedures like epiretinal membrane peeling and retinal vein cannulation require surgeons to manipulate very delicate structures in the eye with little room for error. Many robotic surgery systems have been developed to help surgeons and enforce safeguards during these demanding procedures. One essential piece of information that is required to create and [...]

PhD Speaking Qualifier
PhD Student
Robotics Institute,
Carnegie Mellon University

Affective Robot Behavior Improves Learning in a Sorting Game

GHC 4405

Abstract: Nonverbal communication in the field of education can allow teachers to emotionally support their students and improve educational experience and performance. Robot nonverbal movements have been shown to improve both subjective experiences and task performance, and this work investigates whether affective robot behavior can improve human learning. This is tested using an online sorting [...]

PhD Speaking Qualifier
PhD Student
Robotics Institute,
Carnegie Mellon University

Learning Strategies to Solve Real-World Physics Puzzles

Abstract: In this talk, I focus on efficient online learning for solving real-world physics puzzles. I discuss challenges associated with learning in this domain and how those challenges inform certain design decisions. In particular, learning from scratch in the real world would be difficult. I present a practical mixture of experts framework for learning strategies [...]

PhD Speaking Qualifier
PhD Student
Robotics Institute,
Carnegie Mellon University

Forecasting from LiDAR via Future Object Detection

NSH 3305

Abstract: Object detection and forecasting are fundamental components of embodied perception. These two problems, however, are largely studied in isolation by the community. In this paper, we propose an end-to-end approach for detection and motion forecasting based on raw sensor measurement as opposed to ground truth tracks. Instead of predicting the current frame locations and [...]

PhD Speaking Qualifier
PhD Student
Robotics Institute,
Carnegie Mellon University

Safe control under input limits with neural CBF

NSH 4305

Abstract: In theory, control barrier functions (CBFs) provide a convenient means to construct provably safe controllers. However, a typical problem is that the constructed controller will exceed input limits, and merely clipping the inputs will break all safety guarantees. To address this practical flaw, we consider synthesizing a CBF that will respect input limits. We [...]

PhD Speaking Qualifier
PhD Student
Robotics Institute,
Carnegie Mellon University

Thermal Management Considerations For Lunar Polar Micro-Rovers

GHC 9115

Meeting ID: 940 0396 4889 Passcode: 906118 Abstract:  This research addresses the significant and unprecedented challenge of thermal regulation for lunar polar micro-rovers.  These are distinct from priors by way of very small size, mass, and power, but particularly for the extremes of ambient environment in which they must operate. On the lunar poles, rovers experience temperatures [...]

PhD Speaking Qualifier
PhD Student
Robotics Institute,
Carnegie Mellon University

An Extension to Model Predictive Path Integral Control and Modeling Considerations for Off-road Autonomous Driving in Complex Environment

NSH 3305

Abstract:  The ability to traverse complex environments and terrains is critical to autonomously driving off-road in a fast and safe manner. Challenges such as terrain navigation and vehicle rollover prevention become imperative due to the off-road vehicle configuration and the operating environment itself. This talk will introduce some of these challenges and the different tools [...]

PhD Speaking Qualifier
PhD Student
Robotics Institute,
Carnegie Mellon University

Human-to-Robot Imitation in the Wild

NSH 4305

Abstract: In this talk, I approach the problem of learning by watching humans in the wild. While traditional approaches in Imitation and Reinforcement Learning are promising for learning in the real world, they are either sample inefficient or are constrained to lab settings. Meanwhile, there has been a lot of success in processing passive, unstructured human [...]

PhD Speaking Qualifier
PhD Student
Robotics Institute,
Carnegie Mellon University

Differentiable Collision Detection

NSH 4305

Abstract: Collision detection between objects is critical for simulation, control, and learning for robotic systems. However, existing collision detection routines are inherently non-differentiable, limiting their applications in gradient-based optimization tools. In this talk, I present DCOL: a fast and fully differentiable collision-detection framework that reasons about collisions between a set of composable and highly expressive [...]