PhD Speaking Qualifier
PhD Student
Robotics Institute,
Carnegie Mellon University

NeRF for Robotics

GHC 8102

Abstract: In this talk I'll describe how recent advances in neural rendering and novel view synthesis - namely NeRF - can be leveraged by robotic agents to improve performance in manipulation tasks. Specifically, I'll argue that NeRF can enable robotic policies to: (1) generalize to new viewpoints; (2) perceive specular and reflective surfaces in a [...]

PhD Thesis Defense
Robotics Institute,
Carnegie Mellon University

Search Algorithms and Search Spaces for Neural Architecture Search

NSH 4305

Abstract: Neural architecture search (NAS) is recently proposed to automate the process of designing network architectures. Instead of manually designing network architectures, NAS automatically finds the optimal architecture in a data-driven way. Despite its impressive progress, NAS is still far from being widely adopted as a common paradigm for architecture design in practice. This thesis [...]

MSR Speaking Qualifier
Robotics Institute,
Carnegie Mellon University

MSR Thesis Talk – Evan Harber

Title: Stiffness Mapping of Deformable Objects Through Supervised Embedding and Gaussian Process Regression   Abstract: The stiffness map of a deformable object stores information about that object's surface compliance. Thus, through a stiffness map, we gain insight into the physical properties of that object. Depending on the object, an understanding of stiffness has applications ranging [...]

RI Seminar
Kirstin H. Petersen
Assistant Professor
Electrical & Computer Engineering, College of Engineering, Cornell University

Designing Robotic Systems with Collective Embodied Intelligence

Abstract: Natural swarms exhibit sophisticated colony-level behaviors with remarkable scalability and error tolerance. Their evolutionary success stems from more than just intelligent individuals, it hinges on their morphology, their physical interactions, and the way they shape and leverage their environment. Mound-building termites, for instance, are believed to use their own body as a template for [...]

MSR Speaking Qualifier
PhD Student
Robotics Institute,
Carnegie Mellon University

MSR Thesis Talk – Gaurav Parmar

NSH 1109

Title: Spatially-Adaptive Multilayer GAN Inversion   Abstract: Existing GAN inversion and editing methods are well suited for only a target images that contain aligned objects with a clean background, such as portraits and animal faces, but often struggle for more difficult categories with complex scene layouts and object occlusions, such as cars, animals, and outdoor images. [...]

PhD Speaking Qualifier
PhD Student
Robotics Institute,
Carnegie Mellon University

Robust Reinforcement Learning via Genetic Curriculum

GHC 6501

Abstract: Achieving robust performance is crucial when applying deep reinforcement learning (RL) in safety critical systems. Some of the state of the art approaches try to address the problem with adversarial agents, but these agents often require expert supervision to fine tune and prevent the adversary from becoming too challenging to the trainee agent. While [...]

PhD Speaking Qualifier
PhD Student
Robotics Institute,
Carnegie Mellon University

Mouth Haptics in VR using a Headset Ultrasound Phased Array

GHC 7501

Abstract: This talk is the same one I will be presenting at the ACM CHI Conference on Human Factors in Computing Systems on May 2nd. Paper abstract: Today’s consumer virtual reality (VR) systems offer limited haptic feedback via vibration motors in handheld controllers. Rendering haptics to other parts of the body is an open challenge, [...]

PhD Thesis Proposal
PhD Student
Robotics Institute,
Carnegie Mellon University

Towards Large-scale and Long-term Neural Map Representations

Abstract: We address the problem of large-scale and long-term neural map representations. Maps, as our prior understanding toward the environment, provide valuable information for modern robotics applications such as autonomous driving and AR/VR. The size of maps largely affects the end task performance: usually a more detailed map can support better performance, but would cost [...]

PhD Thesis Defense
Robotics Institute,
Carnegie Mellon University

Self-Improving 3D Scene Representations

GHC 6501

Abstract: Most computer vision models in deployment today are not continually learning. Instead, they are in a “test” mode, where they will behave the same way perpetually, until they are replaced by newer models. This is a problem, because it means the models may perform poorly as soon as their “test” environment diverges from their [...]

MSR Speaking Qualifier
Robotics Institute,
Carnegie Mellon University

MSR Thesis Talk – Manash Pratim Das

TBA

Title: Model-Accuracy Aware Anytime Planning with Simulation Verification for Navigating Complex Terrains Abstract: Off-road and unstructured environments often contain complex patches of various types of terrain, rough elevation changes, deformable objects, etc. An autonomous ground vehicle traversing such environments experiences physical interactions that are extremely hard to model at scale and thus very hard to [...]