Calendar of Events
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2 events,
PhD Thesis Proposal
Planning with Dynamics by Interleaving Search and Trajectory Optimization
Abstract: Search-based planning algorithms enable autonomous agents like robots to come up with well-reasoned long-horizon plans to achieve a given task objective. They do so by searching over the graph that results from discretizing the state and action space. However, in robotics, several dynamically rich tasks require high-dimensional planning in the continuous space. For such […]
VASC Seminar
Fabio Pizzati
Inria
Physics-informed image translation
Abstract: Generative Adversarial Networks (GANs) have shown remarkable performances in image translation, being able to map source input images to target domains (e.g. from male to female, day to night, etc.). However, their performances may be limited by insufficient supervision, which may be challenging to obtain. In this talk, I will present our recent works […]
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1 event,
RI Seminar
Chelsea Finn
Computer Science & Electrical Engineering, Stanford University
Robots Should Reduce, Reuse, and Recycle
Abstract: Despite numerous successes in deep robotic learning over the past decade, the generalization and versatility of robots across environments and tasks has remained a major challenge. This is because much of reinforcement and imitation learning research trains agents from scratch in a single or a few environments, training special-purpose policies from special-purpose datasets. In [...]
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2 events,
PhD Speaking Qualifier
Solving Constraint Tasks with Memory-Based Learning
Abstract: In constraint tasks, the current task state heavily limits what actions are available to an agent. Mechanical constraints exist in many common tasks such as construction, disassembly, and rearrangement and task space constraints exist in an even broader range of tasks. Deep reinforcement learning algorithms have typically struggled with constraint tasks for two main [...]
VASC Seminar
Adriana Kovashka
University of Pittsburgh
Weak Multi-modal Supervision for Object Detection and Persuasive Media
Abstract: The diversity of visual content available on the web presents new challenges and opportunities for computer vision models. In this talk, I present our work on learning object detection models from potentially noisy multi-modal data, retrieving complementary content across modalities, transferring reasoning models across dataset boundaries, and recognizing objects in non-photorealistic media. While the [...]
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1 event,
PhD Speaking Qualifier
Head-Worn Assistive Teleoperation of Mobile Manipulators
Abstract: Mobile manipulators in the home can provide increased autonomy to individuals with severe motor impairments, who often cannot complete activities of daily living (ADLs) without the help of a caregiver. Teleoperation of an assistive mobile manipulator could enable an individual with motor impairments to independently perform self-care and household tasks, yet limited motor function [...]
1 event,
PhD Speaking Qualifier
Text Classification with Class Descriptions Only
Abstract: In this work, we introduce KeyClass, a weakly-supervised text classification framework that learns from class-label descriptions only, without the need to use any human-labeled documents. It leverages the linguistic domain knowledge stored within pre-trained language models and data programming to automatically label documents. We demonstrate its efficacy and flexibility by comparing it to state-of-the-art [...]
1 event,
Faculty Events
RI Faculty Business Meeting
RI Faculty Business Meeting
Meeting for RI Faculty. Discussions include various department topics, policies, and procedures. Generally meets weekly.
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1 event,
2 events,
PhD Speaking Qualifier
Multi-Object Tracking in the Crowd
Abstract: In this talk, I will focus on the problem of multi-object tracking in crowded scenes. Tracking within crowds is particularly challenging due to heavy occlusion and frequent crossover between tracking targets. The problem becomes more difficult when we only have noisy bounding boxes due to background and neighboring objects. Existing tracking methods try to [...]
PhD Thesis Proposal
Utilizing Panoptic Segmentation and a Locally-Conditioned Neural Representation to Build Richer 3D Maps
Abstract: Advances in deep-learning based perception and maturation of volumetric RGB-D mapping algorithms have allowed autonomous robots to be deployed in increasingly complex environments. For robust operation in open-world conditions however, perceptual capabilities are still lacking. Limitations of commodity depth sensors mean that complex geometries and textures cannot be reconstructed accurately. Semantic understanding is still [...]
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2 events,
Faculty Events
NREC Study Group & Recent Projects
This talk will describe the NREC study process that has been developed as a lower cost of entry work product for potential partners. This is a process that is available for anyone on campus that wants to help their sponsors create viable system concepts and potential development costs before committing to a full development program. [...]
RI Seminar
Byron Boots
Machine Learning in the Paul G. Allen School of Computer Science, University of Washington
Machine Learning and Model Predictive Control for Adaptive Robotic Systems
Abstract: In this talk I will discuss several different ways in which ideas from machine learning and model predictive control (MPC) can be combined to build intelligent, adaptive robotic systems. I’ll begin by showing how to learn models for MPC that perform well on a given control task. Next, I’ll introduce an online learning perspective on […]
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1 event,
PhD Speaking Qualifier
Magnification-invariant retinal distance 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 […]
2 events,
Field Robotics Center Seminar
José Luís Silva
Science and Technology Department, University Institute of Lisbon
Towards more effective remote execution of exploration operations using multimodal interfaces
Abstract: Remote robots enable humans to explore and interact with environments while keeping them safe from existing harsh conditions (e.g., in search and rescue, deep sea or planetary exploration scenarios). However, the gap between the control station and the remote robot presents several challenges (e.g., situation awareness, cognitive load, perception, latency) for effective teleoperation. Multimodal […]
PhD Speaking Qualifier
Bridging Humans and Generative Models
Abstract: Deep generative models make visual content creation more accessible to novice and professional users alike by automating the synthesis of diverse, realistic content based on a collected dataset. People often use generative models as data-driven sources, making it challenging to personalize a model easily. Currently, personalizing a model requires careful data curation, which is […]
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1 event,
VASC Seminar
Andrew Owens
Electrical Engineering & Computer Science , University of Michigan
Learning Visual, Audio, and Cross-Modal Correspondences
Abstract: Today's machine perception systems rely heavily on supervision provided by humans, such as labels and natural language. I will talk about our efforts to make systems that, instead, learn from two ubiquitous sources of unlabeled data: visual motion and cross-modal sensory associations. I will begin by discussing our work on creating unified models for […]
2 events,
PhD Speaking Qualifier
Impulse considerations for reasoning about intermittent contacts
Abstract: Many of our interactions with the environment involve making and breaking contacts. However, it is not always obvious how one should reason about these intermittent contacts (sequence, timings, locations) in an online and adaptive way. This is particularly relevant in gait generation for legged locomotion control, where it is standard to simply predefine and […]
PhD Thesis Proposal
Multi-Human 3D Reconstruction from Monocular RGB Videos
Abstract: We study the problem of multi-human 3D reconstruction from RGB videos captured in the wild. Humans have dynamic motion, and reconstructing them in arbitrary settings is key to building 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 […]
1 event,
PhD Thesis Proposal
Learning and Translating Temporal Abstractions across Humans and Robots
Abstract: Humans possess a remarkable ability to learn to perform tasks from a variety of different sources-from language, instructions, demonstration, etc. In each case, they are able to easily extract the high-level strategy to solve the task, such as the recipe of cooking a dish, whilst ignoring irrelevant details, such as the precise shape of […]
3 events,
PhD Speaking Qualifier
Robust Incremental Smoothing and Mapping
Abstract: In this work we present a method for robust optimization for online incremental Simultaneous Localization and Mapping (SLAM). Due to the NP-Hardness of data association in the presence of perceptual aliasing, tractable (approximate) approaches to data association will produce erroneous measurements. We require SLAM back-ends that can converge to accurate solutions in the presence […]
PhD Thesis Defense
3D Reconstruction using Differential Imaging
Abstract: 3D reconstruction has been at the core of many computer vision applications, including autonomous driving, visual inspection in manufacturing, and augmented and virtual reality (AR/VR). Because monocular 3D sensing is fundamentally ill-posed, many techniques aiming for accurate reconstruction use multiple captures to solve the inverse problem. Depending on the amount of change in these […]
PhD Thesis Defense
Jacky Liang
Learning with Structured Priors for Robust Robot Manipulation
Abstract: Robust and generalizable robots that can autonomously manipulate objects in semi-structured environments can bring material benefits to society. Data-driven learning approaches are crucial for enabling such systems by identifying and exploiting patterns in semi-structured environments, allowing robots to adapt to novel scenarios with minimal human supervision. However, despite significant prior work in learning for […]
4 events,
MSR Thesis Defense
Learning Parameter-Efficient Quadrotor Dynamics Models
Abstract: Operation of quadrotors through high-speed, high-acceleration maneuvers remains a challenging problem due to the complex aerodynamics in this regime. While standard physical models suffice for control in near-hover conditions, the primary challenge in executing aggressive trajectories is obtaining a model for the quadrotor dynamics that adequately models the aerodynamic effects present, including lift, drag, […]
Faculty Events
RI Faculty Business Meeting
RI Faculty Business Meeting
Meeting for RI Faculty. Discussions include various department topics, policies, and procedures. Generally meets weekly.
PhD Thesis Defense
Self-Supervising Occlusions For Vision
Abstract: Virtually every scene has occlusions. Even a scene with a single object exhibits self-occlusions - a camera can only view one side of an object (left or right, front or back), or part of the object is outside the field of view. More complex occlusions occur when one or more objects block part(s) of […]
Carnegie Mellon University
Carnegie Mellon University