Calendar of Events
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3 events,
VASC Seminar
Qitao Zhao
Computer Vision, Carnegie Mellon University
Sparse-view Pose Estimation and Reconstruction via Analysis by Generative Synthesis
Abstract: This talk will present our approach for reconstructing objects from sparse-view images captured in unconstrained environments. In the absence of ground-truth camera poses, we will demonstrate how to utilize estimates from off-the-shelf systems and address two key challenges: refining noisy camera poses in sparse views and effectively handling outlier poses. Bio: Qitao is a second-year […]
VASC Seminar
Vimal Mollyn
Human Computer Interaction Institute, Carnegie Mellon University
EgoTouch: On-Body Touch Input Using AR/VR Headset Cameras
Abstract: In augmented and virtual reality (AR/VR) experiences, a user’s arms and hands can provide a convenient and tactile surface for touch input. Prior work has shown on-body input to have significant speed, accuracy, and ergonomic benefits over in-air interfaces, which are common today. In this work, we demonstrate high accuracy, bare hands (i.e., no special […]
VASC Seminar
Hyunsung Cho
Human-Computer Interaction Institute (HCII) , Carnegie Mellon University
Auptimize: Optimal Placement of Spatial Audio Cues for Extended Reality
Abstract: Spatial audio in Extended Reality (XR) provides users with better awareness of where virtual elements are placed, and efficiently guides them to events such as notifications, system alerts from different windows, or approaching avatars. Humans, however, are inaccurate in localizing sound cues, especially with multiple sources due to limitations in human auditory perception such as […]
2 events,
MSR Thesis Defense
VoxDet: Voxel Learning for Novel Instance Detection
Abstract: Detecting unseen instances based on multi-view templates is a challenging problem due to its open-world nature. Traditional methodologies, which primarily rely on 2D representations and matching techniques, are often inadequate in handling pose variations and occlusions. To solve this, we introduce VoxDet, a pioneer 3D geometry-aware framework that fully utilizes the strong 3D voxel […]
MSR Thesis Defense
Voxel Learning for Novel Instance Detection
Abstract: Detecting unseen instances based on multi-view templates is a challenging problem due to its open-world nature. Traditional methodologies, which primarily rely on 2D representations and matching techniques, are often inadequate in handling pose variations and occlusions. To solve this, we introduce VoxDet, a pioneer 3D geometry-aware framework that fully utilizes the strong 3D voxel […]
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1 event,
PhD Thesis Proposal
Sensorimotor-Aligned Design for Pareto-Efficient Haptic Immersion in Extended Reality
Abstract: A new category of computing devices is emerging: augmented and virtual reality headsets, collectively referred to as extended reality (XR). These devices can alter, augment, or even replace our reality. While these headsets have made impressive strides in audio-visual immersion over the past half-century, XR interactions remain almost completely absent of appropriately expressive tactile […]
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1 event,
PhD Thesis Proposal
Evaluating and Improving Vision-Language Models Beyond Scaling Laws
Abstract: In this talk, we present our work on advancing Vision-Language Models (VLMs) beyond scaling laws through improved evaluation and (post-)training strategies. Our contributions include VQAScore, a state-of-the-art alignment metric for text-to-visual generation. We show how VQAScore improves visual generation under real-world user prompts in GenAI-Bench. Additionally, we explore training methods that leverage the language […]
2 events,
PhD Thesis Defense
Whisker-Inspired Sensors for Unstructured Environments
Abstract: Robots lack the perception abilities of animals, which is one reason they can not achieve complex control in outdoor unstructured environments with the same ease as animals. One cause of the perception gap is the constraints researchers place on the environments in which they test new sensors so algorithms can correctly interpret data from […]
PhD Speaking Qualifier
Strategy and Skill Learning for Physics-based Table Tennis Animation
Abstract: Recent advancements in physics-based character animation leverage deep learning to generate agile and natural motion, enabling characters to execute movements such as backflips, boxing, and tennis. However, reproducing the selection and use of diverse motor skills in dynamic environments to solve complex tasks, as humans do, still remains a challenge. We present a strategy […]
1 event,
RI Seminar
Nils Napp
Electrical and Computer Engineering, Cornell University
Abstraction Barriers for Embodied Algorithms
Abstract: Designing robotic systems to reliably modify their environment typically requires expert engineers and several design iterations. This talk will cover abstraction barriers that can be used to make the process of building such systems easier and the results more predictable. By focusing on approximate mathematical representations that model the process dynamics, these representations can […]
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PhD Thesis Proposal
Getting Optimization layers to play well with Deep Networks: Numerical methods and Architectures
Abstract: Many real-world challenges, from robotic control to resource management, can be effectively formulated as optimization problems. Recent advancements have focused on incorporating these optimization problems as layers within deep learning pipelines, enabling the explicit inclusion of auxiliary constraints or cost functions, which is crucial for applications such as enforcing physical laws, ensuring safety constraints, [...]
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2 events,
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1 event,
Field Robotics Center Seminar
A retrospective, 40 Years of Field Robotics
Abstract: Chuck has been building and deploying robots in the field for the past 40 years. In this retrospective he will touch on the robots, people and experiences that have been part of the journey. From the early days in the 1980s with the Three Mile Island nuclear robots and the first outdoor autonomy robots [...]
1 event,
PhD Thesis Proposal
Data Attribution for Text-to-Image Models
Abstract: Large text-to-image models learn from training data to synthesize "novel" images, but how the models use the training data remains a mystery. The problem of data attribution is to identify which training images are influential for generating a given output. Specifically, removing influential images and retraining the model would prevent it from reproducing that [...]
