RI Picnic
The RI Picnic will be held at the Vietnam Veteran's Pavilion @ Schenley Park on Overlook Drive, Tuesday, August 29, 1-7pm. SOCIALIZE, EAT, DRINK & BE MERRY! Receive this year's RI giveaway item; witness the exciting final rounds of the annual RI croquet tournament; enjoy lawn games right at our own pavilion area. Plan to spend some time at the [...]
Continual Robot Learning: Benchmarks and Modular Methods
Abstract: Humans adapt continuously to the world around us, allowing us to acquire new skills and explore diverse environments seamlessly. Current AI methods, however, cannot attain this versatility. Instead, they are typically trained with vast datasets, and learn all tasks simultaneously. However, the trained models have limited ability to adapt to changing contexts, and are [...]
Architecture and Algorithms for Space-Based Global Wildlife Tracking
Abstract: Accurate satellite based positioning revolutionized several industries over the past two decades from agriculture to transportation. However, conventional GNSS receivers consume significant amounts of energy and are too large for many applications, including wildlife-tracking which is critical for conservation efforts and improving our understanding of the global climate. To address this capability gap, we [...]
Multi-Human 3D Reconstruction from Monocular Videos
Abstract: We study the problem of multi-human 3D reconstruction from videos captured in the wild. Human movements are dynamic, and accurately reconstructing them in various settings is crucial for developing immersive social telepresence, assistive humanoid robots, and augmented reality systems. However, creating such a system requires addressing fundamental issues with previous works regarding the data [...]
Language-Conditioned Object Detection and Manipulation
Abstract: Traditional object detection methods are often confined to predefined object vocabularies, limiting their versatility in real-world scenarios where robots need to understand and execute diverse household tasks. Additionally, the 2D and 3D perception communities have typically pursued separate approaches tailored to their respective domains. In this thesis, we present a language-conditioned object detector with [...]
How I Learned to Love Blobs: The Power of Gaussian Representations in Differentiable Rendering and Optimization
Abstract: In this thesis, we explore the use of Gaussian Representations in multiple application areas of computer vision and robotics. In particular, we design a ray-based differentiable renderer for 3D Gaussians that can be used to solve multiple classic computer vision problems in a unified manner. For example, we can reconstruct 3D shapes from color, [...]
Watch, Practice, Improve: Towards In-the-wild Manipulation
Abstract: The longstanding dream of many roboticists is to see robots perform diverse tasks in diverse environments. To build such a robot that can operate anywhere, many methods train on robotic interaction data. While these approaches have led to significant advances, they rely on heavily engineered setups or high amounts of supervision, neither of which [...]
Generating Beautiful Pixels
Abstract: In this talk, I will present three experiments that use low-level image statistics to generate high-resolution detailed outputs. In the first experiment, I will use 2D pixels to efficiently mine hard examples for better learning. Simply biasing ray sampling towards hard ray examples enables learning of neural fields with more accurate high-frequency detail in less [...]
Towards Reliable Computer Vision Systems
Abstract: The real world has infinite visual variation – across viewpoints, time, space, and curation. As deep visual models become ubiquitous in high-stakes applications, their ability to generalize across such variation becomes increasingly important. In this talk, I will present opportunities to improve such generalization at different stages of the ML lifecycle: first, I will [...]
Towards Photorealistic Dynamic Capture and Animation of Human Hair and Head
Abstract: Realistic human avatars play a key role in immersive virtual telepresence. To reach a high level of realism, a human avatar needs to faithfully reflect human appearance. A human avatar should also be drivable and express natural motions. Existing works have made significant progress in building drivable realistic face avatars, but they rarely include [...]
Modeling Dynamic Clothing for Data-Driven Photorealistic Avatars
Abstract: In this thesis, we aim to build photorealistic animatable avatars of humans wearing complex clothing in a data-driven manner. Such avatars will be a critical technology to enable future applications such as immersive telepresence in Virtual Reality (VR) and Augmented Reality (AR). Existing full-body avatars that jointly model geometry and view-dependent texture using Variational [...]
Manipulation Among Movable Objects for Pick-and-Place Tasks in Cluttered 3D Workspaces
Abstract: In cluttered real-world workspaces, simple pick-and-place tasks for robot manipulators can be quite challenging to solve. Often there is no collision-free trajectory that allows the robot to grasp and extract a desired object from the scene. This requires motion planning algorithms to reason about rearranging some of the “movable” clutter in the scene so [...]
Transforming Hollywood Visual Effects with Graphics and Vision
Abstract: Paul will describe his path to developing visual effects technology used in hundreds of movies, including The Matrix, Spider-Man 2, Benjamin Button, Avatar, Maleficent, Furious 7, and Blade Runner: 2049. These techniques include image-based modeling and rendering, high dynamic range imaging, image-based lighting, and high-resolution facial scanning for photoreal digital actors. Paul will also [...]
Vision without labels
Abstract: Deep learning has revolutionized all aspects of computer vision, but its successes have come from supervised learning at scale: large models trained on ever larger labeled datasets. However this reliance on labels makes these systems fragile when it comes to new scenarios or new tasks where labels are unavailable. This is in stark contrast to [...]
RI Faculty Business Meeting
Meeting for RI Faculty. Discussions include various department topics, policies, and procedures. Generally meets weekly.
Learning Meets Gravity: Robots that Learn to Embrace Dynamics from Data
Abstract: Despite the incredible capabilities (speed and repeatability) of our hardware today, many robot manipulators are deliberately programmed to avoid dynamics – moving slow enough so they can adhere to quasi-static assumptions of the world. In contrast, people frequently (and subconsciously) make use of dynamic phenomena to manipulate everyday objects – from unfurling blankets, to [...]
Large Multimodal (Vision-Language) Models for Image Generation and Understanding
Abstract: Large Language Models and Large Vision Models, also known as Foundation Models, have led to unprecedented advances in language understanding, visual understanding, and AI. In particular, many computer vision problems including image classification, object detection, and image generation have benefited from the capabilities of such models trained on internet-scale text and visual data. In [...]
RI Faculty Business Meeting
Meeting for RI Faculty. Discussions include various department topics, policies, and procedures. Generally meets weekly.
Learning and Control for Safety, Efficiency, and Resiliency of Embodied AI
Abstract: The rapid evolution of ubiquitous sensing, communication, and computation technologies has revolutionized of cyber-physical systems (CPS) across virous domains like robotics, smart grids, aerospace, and smart cities. Integrating learning into dynamic systems control presents significant Embodied AI opportunities. However, current decision-making frameworks lack comprehensive understanding of the tridirectional relationship among communication, learning and control, [...]
Generalizable Dexterity with Reinforcement Learning
Abstract: Dexterity, the ability to perform complex interactions with the physical world, is at the core of robotics. However, existing research in robot manipulation has been focused on tasks that involve limited dexterity, such as pick-and-place. The motor skills of the robots are often quasi-static, have a predefined or limited sequence of contact events, and [...]
Imaginative Vision Language Models: Towards human-level imaginative AI skills transforming species discovery, content creation, self-driving cars, and emotional health
Abstract: Most existing AI learning methods can be categorized into supervised, semi-supervised, and unsupervised methods. These approaches rely on defining empirical risks or losses on the provided labeled and/or unlabeled data. Beyond extracting learning signals from labeled/unlabeled training data, we will reflect in this talk on a class of methods that can learn beyond the vocabulary [...]
World Knowledge in the Time of Large Models
Abstract: This talk will discuss the massive shift that has come about in the vision and ML community as a result of the large pre-trained language and language and vision models such as Flamingo, GPT-4, and other models. We begin by looking at the work on knowledge-based systems in CV and robotics before the large model [...]
Data-Efficient Learning for Robotics and Reinforcement Learning
Abstract: Data efficiency, i.e., learning from small datasets, is of practical importance in many real-world applications and decision-making systems. Data efficiency can be achieved in multiple ways, such as probabilistic modeling, where models and predictions are equipped with meaningful uncertainty estimates, transfer learning, or the incorporation of valuable prior knowledge. In this talk, I will [...]
RI Faculty Business Meeting
Meeting for RI Faculty. Discussions include various department topics, policies, and procedures. Generally meets weekly.
Digital Human Modeling with Light
Abstract: Leveraging light in various ways, we can observe and model physical phenomena or states which may not be possible to observe otherwise. In this talk, I will introduce our recent exploration on digital human modeling with different types of light. First, I will present our recent work on the modeling of relightable human heads, [...]
Preference Based Optimization of Multi-Objective Robot Performance
Abstract: Robotic systems often require that tradeoffs be made--for example, between performance and robustness, power and longevity, or efficiency and safety. While roboticists can design cost functions with hand-picked weights for different metrics, it is not always a straightforward task, particularly when some aspects of performance are not easily quantified. This can occur especially when [...]
Dynamic 3D Gaussians: Tracking by Persistent Dynamic View Synthesis
Abstract: We present a method that simultaneously addresses the tasks of dynamic scene novel-view synthesis and six degree-of-freedom (6-DOF) tracking of all dense scene elements. We follow an analysis-by-synthesis framework, inspired by recent work that models scenes as a collection of 3D Gaussians which are optimized to reconstruct input images via differentiable rendering. To model [...]
RI Faculty Business Meeting
Meeting for RI Faculty. Discussions include various department topics, policies, and procedures. Generally meets weekly.
Ensuring safety for uncertain high-dimensional robotic systems
Abstract: Two major obstacles for safe control and planning are (1) scaling to high-dimensional systems and (2) handling uncertain systems. This is problematic because such systems are ubiquitous in practice: e.g. drones with unknown drag, manipulators carrying unknown packages. In this proposal, we aim to address both challenges. At the control level, we have synthesized [...]
Trustworthy Learning using Uncertain Interpretation of Data
Abstract: Non-parametric models are popular in real-world applications of machine learning. However, many modern ML methods that ensure that models are pragmatic, safe, robust, fair, and otherwise trustworthy in increasingly critical applications, assume parametric, differentiable models. We show that, by interpreting data as locally uncertain, we can achieve many of these without being limited to [...]
Allocation, Planning, and Control in Off-road Automated Convoy Operations
Abstract: The lack of structure in off-road terrains makes off-road operations of automated platforms difficult. The difficulty arises from uncertainty in the optimality and safety of the actions (e.g., planning and control) taken by the automated platform. When multiple automated platforms are required to act in a coordinated manner (e.g., a convoy) in complex cluttered [...]