MSR Speaking Qualifier
Robotics Institute,
Carnegie Mellon University

MSR Thesis Talk – Naman Gupta

GHC 6501

Title: State Estimation for Legged Robots using Proprioceptive Sensors   Abstract:  Mobile robots need good estimates of their state to perform closed-loop control in structured and unstructured environments. A number of existing algorithms rely on data fusion from multiple sensors to compute these estimates. This work focuses on state estimation using sensors which only measure [...]

VASC Seminar
Yuval Bahat
PhD
Technion - Israel Institute of Technology

Exploiting Deviations from Ideal Visual Recurrence

1305 Newell Simon Hall

Abstract: Visual repetitions are abundant in our surrounding physical world: small image patches tend to reoccur within a natural image, and across different rescaled versions thereof. Similarly, semantic repetitions appear naturally inside an object class within image datasets, as a result of different views and scales of the same object. We studied deviations from these [...]

MSR Speaking Qualifier
Robotics Institute,
Carnegie Mellon University

MSR Thesis Talk – Harjatin Baweja

NSH 1305

Title: Leveraging Computer Vision and Reinforcement Learning for contact and non-contact based plant phenotyping.     Abstract:  Effective plant breeding requires scientists to find correspondences between genetic markers and desirable physical traits (phenotypes) of the genotype. Robotics can aid the acceleration of breeding pipeline by facilitating high throughput plant phenotyping. In this thesis we propose [...]

MSR Speaking Qualifier
Robotics Institute,
Carnegie Mellon University

MSR Thesis Talk – Suhit Kodgule

Title: Active Sampling for Planetary Rover Exploration Abstract: Planetary Robotics research has expanded beyond simply developing robust navigation strategies for rovers to providing them with the capability of performing intelligent actions so as to develop a better interpretation and understanding of the environment. This will become essential in the future, when rovers explore regions far [...]

VASC Seminar
Shu Kong
PhD Candidate
University of California at Irvine

Attending to Pixels, Embedding Pixels, Predicting Pixels

1305 Newell Simon Hall

Abstract: Nowadays splashy applications heavily depend on meticulously annotated datasets, data-driven and learning-based methods, among which pixel labeling plays an important role yet often lacks interpretability. In this talk, I will discuss how we deal with pixels with better interpretability. Firstly, I'll introduce the pixel embedding framework that allows for clustering pixels into discrete groups [...]

MSR Speaking Qualifier
Robotics Institute,
Carnegie Mellon University

Matthew Collins – MSR Thesis Talk

NSH 3002

Title:  Efficient Planning for High-Speed MAV Flight in Unknown Environments Using Sparse Topological Graphs   Abstract: Safe high-speed autonomous navigation for MAVs in unknown environments requires fast planning to enable the robot to adapt and react quickly to incoming information about obstacles within the world.  Furthermore, when operating in environments not known a priori, the robot [...]

MSR Speaking Qualifier
Robotics Institute,
Carnegie Mellon University

MSR Thesis Talk – Siva Chaitanya Mynepalli

Title: Recognizing Tiny Faces Abstract: Objects are naturally captured over a continuous range of distances, causing dramatic changes in appearance, especially at low resolutions. Recognizing such small objects at range is an open challenge in object recognition. In this paper, we explore solutions to this problem by tackling the fine-grained task of face recognition. State-of-the-art embeddings [...]

PhD Thesis Defense
Robotics Institute,
Carnegie Mellon University

Data Centric Robot Learning

NSH 4305

Abstract: While robotics has made tremendous progress over the last few decades, most success stories are still limited to carefully engineered and precisely modeled environments. Getting these robots to work in the complex and diverse world that we live in has proven to be a difficult challenge. Interestingly, one of the most significant successes in [...]

VASC Seminar
Erik Learned-Miller
Professor
University of Massachusetts, Amherst

Automatically Supervised Learning: Two more steps on a long journey

1305 Newell Simon Hall

Abstract: I will talk about two recent pieces of work that attempt to move towards learning with less reliance on labeled data. In the first, part, I will talk about how the surrogate task of predicting the motion of objects can induce complex representations in neural networks without any labeled data.  In the second part of [...]