Calendar of Events
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2 events,
PhD Thesis Proposal
Ramkumar Natarajan
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 […]
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 [...]
2 events,
PhD Speaking Qualifier
Mrinal Verghese
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 [...]
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.
2 events,
Faculty Events
Gabriel Goldman
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 […]
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
Sheng-Yu Wang
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 […]
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 […]
3 events,
PhD Speaking Qualifier
Daniel McGann
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
Shumian Xin
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
Suvansh Sanjeev
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
Dinesh Reddy Narapureddy
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