MSR Speaking Qualifier
Robotics Institute,
Carnegie Mellon University

Rohith Pillai – MSR Thesis Talk

Zoom

ZOOM Link: https://cmu.zoom.us/j/95344974779?pwd=aXlmbktDMFZIUjhyeTRuNWxmeXcwdz09 Meeting ID: 953 4497 4779 Passcode: 783497   Title:  3D Face Reconstruction from Monocular Video and its Applications In the Wild   Abstract: 3D face reconstruction is a very popular field of computer vision due to its applications in social media, entertainment and health. However, ever since the introduction of 3D morphable models as [...]

PhD Speaking Qualifier
PhD Student
Robotics Institute,
Carnegie Mellon University

A Graph-Based Method for Joint Instance Segmentation of Point Clouds and Image Sequences

Abstract: While learning-based semantic instance segmentation methods have achieved impressive progress, their use is limited in robotics applications due to reliance on expensive training data annotations and assumptions of single sensor modality or known object classes. We propose a novel graph-based instance segmentation approach that combines information from a 2D image sequence and a 3D [...]

PhD Speaking Qualifier
PhD Student
Robotics Institute,
Carnegie Mellon University

Continual Reinforcement Learning using Self-Activating Neural Ensembles

Abstract: The ability for an agent to continuously learn new skills without catastrophically forgetting existing knowledge is of critical importance for the development of generally intelligent agents. Most methods devised to address this problem depend heavily on well-defined task boundaries which simplify the problem considerably. Our task-agnostic method, Self-Activating Neural Ensembles (SANE), uses a hierarchical [...]

PhD Speaking Qualifier
Robotics Institute,
Carnegie Mellon University

Unsupervised 2D-3D Lifting with Deep Structure Priors

Abstract: Learning to estimate non-rigid 3D structures from 2D imaged observations is bottle-necked by the availability of abundant 3D annotated data. Learning methods that reduce the amount of required annotation is of high practical value. In this regard, Non-Rigid Structure from Motion (NRSfM) methods offer the opportunity to infer 3D structures solely from 2D annotations. [...]

PhD Speaking Qualifier
PhD Student
Robotics Institute,
Carnegie Mellon University

Model Adaptation for Compliant Parallel Robot with Nonstationary Dynamics

Abstract: Soft robots can be constructed with few parts and from a wide variety of materials. This makes them a potentially appealing choice for applications where there are resource constraints on system fabrication. However, soft robot dynamics are difficult to accurately model analytically, due to a multiphysics coupling between shape, forces, temperature, and history of [...]

PhD Speaking Qualifier
PhD Student
Robotics Institute,
Carnegie Mellon University

Adaptive Safety Margins for Safe Replanning 
under Time-Varying Disturbances

Abstract: Safe real-time navigation is a considerable challenge because engineers often need to work with uncertain vehicle dynamics, variable external disturbances, and imperfect controllers. A common strategy used to address safety is to employ hand-defined margins for obstacle inflation. However, arbitrary static margins often fail in more dynamic scenarios, and using worst-case assumptions proves to [...]

PhD Speaking Qualifier
PhD Student
Robotics Institute,
Carnegie Mellon University

HyperDynamics: Generating Expert Dynamics Models by Observation

Abstract: We propose HyperDynamics, a framework that conditions on an agent’s interactions with the environment and optionally its visual observations, and generates the parameters of neural dynamics models based on inferred properties of the dynamical system. Physical and visual properties of the environment that are not part of the low-dimensional state yet affect its temporal [...]

PhD Speaking Qualifier
PhD Student
Robotics Institute,
Carnegie Mellon University

Direct Fitting of Mixture Models

Abstract: There exist many choices of 3D shape representation. Some recent work has advocated for the use of Gaussian Mixture Models as a compact representation for 3D shapes and scenes. These models are typically fit to point clouds, even when the shapes were obtained as 3D meshes. Here we present a formulation for fitting Gaussian [...]

PhD Speaking Qualifier
Principal Research Programmer / Analyst
Robotics Institute,
Carnegie Mellon University

Terrain Perception using Structured Light for Micro-Rovers

Abstract: With continuing advancement in technology, the future of planetary exploration is likely to be dominated by robotic missions. Yet rovers capable of science investigations are slow and bulky with very limited computing which prohibits demonstrating full autonomy. These rovers are also risk averse due to their huge mission cost. However there is a new [...]