Carnegie Mellon University
Transparency in Deep Reinforcement Learning Networks
In the recent years there has been a growing interest in the field of Explainability for machine learning models in general and deep learning in particular. This is because, deep learning based approaches have made tremendous progress in the field of computer vision, reinforcement learning, language related domains and are being increasingly used in application areas [...]
Carnegie Mellon University
Geometric approaches to motion planning for two classes of low-Reynolds number swimmers
Microrobots have the potential to impact many areas of medicine such as microsurgery, targeted drug delivery and minimally invasive sensing. Just like microorganisms themselves, microrobots developed for these applications need to swim in a low-Reynolds number regime which warrants locomotive strategies that differ from their macroscopic counterparts. To this end, Purcell’s three-link planar swimmer has [...]
Carnegie Mellon University
Autonomous 3D Reconstruction in Underwater Unstructured Scenes
Abstract Reconstruction of marine structures such as pilings underneath piers presents a plethora of interesting challenges. It is one of those tasks better suited to a robot due to harsh underwater environments. Underwater reconstruction typically involves human operators remotely controlling the robot to predetermined way-points based on some prior knowledge of the location and model [...]
Carnegie Mellon University
Wire Detection, Reconstruction, and Avoidance for Unmanned Aerial Vehicles
Abstract Thin objects, such as wires and power lines are one of the most challenging obstacles to detect and avoid for UAVs, and are a cause of numerous accidents each year. This thesis makes contributions in three areas of this domain: wire segmentation, reconstruction, and avoidance. Pixelwise wire detection can be framed as a binary [...]
Carnegie Mellon University
Multi-Robot Routing and Scheduling with Spatio-Temporal And Ordering Constraints
Abstract We consider the problem of allocation and routing a fleet of robots to service a given set of locations while minimizing makespan. The service start times for the locations are subject to AND/OR type precedence constraints. Spatio-temporal constraints prohibit certain states from all feasible schedules where a state is defined as a tuple of [...]
The Art of Robotics: Toward a Holistic Approach
I arrived at the Robotics Institute two years ago looking for a good project, something tangible and preferably related to legged locomotion. Instead, I met Matt Mason and started to think about the big picture, ask the big questions. What is manipulation? What is robotics? What makes robotics particularly hard? To answer these questions, I [...]
Carnegie Mellon University
Mapping gamma sources and their flux fields using non-directional flux measurements
There is a compelling need to determine the location and activity of radiation sources from the flux that they generate. There is also a need to create dense flux maps from sparse measurements. This research solves these dual problems. An example of a situation where these capabilities would be vital is at the location of [...]
Carnegie Mellon University
Automated Design of Special Purpose Dexterous Manipulators
Grasp planning and motion synthesis for dexterous manipulation tasks are traditionally done given a pre-existing kinematic model for the robotic hand. In this thesis, we introduce a framework for automatically designing hand topologies best suited for manipulation tasks given high level objectives as input. Our goal is to ultimately design a program that is able [...]
Carnegie Mellon University
Toward Invariant Visual Inertial State Estimation using Information Sparsification
Abstract In this work, we address two current challenges in real-time visual-inertial odometry (VIO) systems - efficiency and accuracy. To this end, we present a novel approach to tightly couple visual and inertial measurements in a fixed-lag VIO framework using information sparsification. To bound computational complexity, fixed-lag smoothers perform marginalization of variables but consequently deteriorate accuracy and [...]
Generative Point Cloud Modeling with Gaussian Mixture Models for Multi-Robot Exploration
Autonomous exploration in rich 3D environments requires the construction and maintenance of a representation derived from accumulated 3D observations. Volumetric models, which are commonly employed to enable joint reasoning about occupied and free space, scale poorly with the size of the environment. Techniques employed to mitigate this scaling include hierarchical discretization, learning local data summarizations [...]