PhD Thesis Proposal
Robotics Institute,
Carnegie Mellon University

Robotic Grasping in the Wild

Zoom Link Abstract Robotics and artificial intelligence have witnessed tremendous progress in the past decade. Yet, we are still far from building the general purpose robot butler that can autonomously operate in homes and help with manipulation tasks like household chores. Grasping is an important action primitive for manipulation and needs to generalize to unstructured [...]

PhD Speaking Qualifier
Robotics Institute,
Carnegie Mellon University

Resource-constrained learning and inference for visual perception

Zoom Link Abstract Real-world applications usually require computer vision algorithms to meet certain resource constraints. In this talk, I will present evaluation methods and principled solutions for both cases of training and testing. First, I will talk about a formal setting for studying training under the non-asymptotic, resource-constrained regime, i.e., budgeted training. We analyze the [...]

PhD Thesis Defense
Robotics Institute,
Carnegie Mellon University

Multi-hypothesis iSAM2 for Ambiguity-aware Passive and Active SLAM

Archived video Abstract Simultaneous localization and mapping (SLAM) is the problem of estimating the state of a moving agent with sensors on it while simultaneously reconstructing a map of its surrounding environment, which has been a popular research field due to its wide applications. As many state-of-the-art SLAM algorithms can already achieve high accuracy in [...]

VASC Seminar
Jia-Bin Huang
Assistant Professor
Virginia Tech

Learning to See Through Occlusions and Obstructions

Virtual VASC:  https://cmu.zoom.us/j/249106600   Abstract:  Photography allows us to capture and share memorable moments of our lives. However, 2D images appear flat due to the lack of depth perception and may suffer from poor imaging conditions such as taking photos through reflecting or occluding elements. In this talk, I will present our recent efforts to [...]

PhD Thesis Defense
Robotics Institute,
Carnegie Mellon University

Terrain Relative Navigation for Lunar Polar Roving: Exploiting Geometry, Shadows, and Planning

Archived Zoom Video Abstract Water ice at the lunar poles could be the most valuable resource beyond planet Earth. However, that value is not foregone, and can only be determined by rovers that evaluate the distributions of abundance, concentration, and characteristics of this ice. The near-term explorations will be solar and unlikely to endure night, [...]

PhD Thesis Defense
Robotics Institute,
Carnegie Mellon University

Resource-Constrained State Estimation with Multi-Modal Sensing

Zoom Link Accurate and reliable state estimation is essential for safe mobile robot operation in real-world environments because ego-motion estimates are required by many critical autonomy functions such as control, planning, and mapping. Computing accurate state estimates depends on the physical characteristics of the environment, the selection of suitable sensors to capture that information, and [...]

VASC Seminar
Yuxin Wu
Research Engineer
Facebook AI Research

Detectron2 in Object Detection Research

Virtual VASC:  https://cmu.zoom.us/j/249106600   Abstract:  Detectron2 is Facebook's library for object detection and segmentation. It has been used widely in FAIR's research and Facebook's products. This talk will introduce detectron2 with a focus on its use in object detection research, including the lessons we learned from building it, as well as the new research enabled [...]

MSR Speaking Qualifier
Robotics Institute,
Carnegie Mellon University

Aditya Dhawale – MSR Thesis Talk

TBA

Title: Hierarchical Gaussian Distributions for Real-Time SLAM ZOOM Link: (Virtual Presentation) https://cmu.zoom.us/j/7210519673 Abstract: We present Gaussian distributions as structure primitives in a hierarchical multi-fidelity framework to enable accurate real-time Simultaneous Localization and Mapping (SLAM) using uncertain depth data. Real-time mapping and localization capabilities on a mobile robot can enable deployment of robots in real-world scenarios. An autonomous system must [...]

VASC Seminar
Olga Russakovsky
Assistant Professor
Department of Computer Science, Princeton University

Fairness in visual recognition

Virtual VASC Seminar:  https://cmu.zoom.us/j/249106600   Abstract: Computer vision models trained on unparalleled amounts of data hold promise for making impartial, well-informed decisions in a variety of applications. However, more and more historical societal biases are making their way into these seemingly innocuous systems. Visual recognition models have exhibited bias by inappropriately correlating age, gender, sexual [...]

PhD Speaking Qualifier
Robotics Institute,
Carnegie Mellon University

Planning and Execution using Inaccurate Models with Provable Guarantees

Zoom Link Abstract: Models used in modern planning problems to simulate outcomes of real world action executions are becoming increasingly complex, ranging from simulators that do physics-based reasoning to precomputed analytical motion primitives. However, robots operating in the real world often face situations not modeled by these models before execution. This imperfect modeling can lead [...]