Affordance-based control of a variable-autonomy telerobot

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2012.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis. "September 2012."Includes bibliographical references (pages 37-38).Most robot platforms operate in one of two modes: full autonomy, usually in the lab; or low-level teleoperation, usually in the field. Full autonomy is currently realizable only in narrow domains of robotics-like mapping an environment. Tedious teleoperation/joystick control is typical in military applications, like complex manipulation and navigation with bomb-disposal robots. This thesis describes a robot "surrogate" with an intermediate and variable level of autonomy. The robot surrogate accomplishes manipulation tasks by taking guidance and planning suggestions from a human "supervisor." The surrogate does not engage in high-level reasoning, but only in intermediate-level planning and low-level control. The human supervisor supplies the high-level reasoning and some intermediate control-leaving execution details for the surrogate. The supervisor supplies world knowledge and planning suggestions by "drawing" on a 3D view of the world constructed from sensor data. The surrogate conveys its own model of the world to the supervisor, to enable mental-model sharing between supervisor and surrogate. The contributions of this thesis include: (1) A novel partitioning of the manipulation task load between supervisor and surrogate, which side-steps problems in autonomous robotics by replacing them with problems in interfaces, perception, planning, control, and human-robot trust; and (2) The algorithms and software designed and built for mental model-sharing and supervisor-assisted manipulation. Using this system, we are able to command the PR2 to manipulate simple objects incorporating either a single revolute or prismatic joint.by Michael Fleder.M. Eng

Autonomous Rover Traverse and Precise Arm Placement on Remotely Designated Targets

This software controls a rover platform to traverse rocky terrain autonomously, plan paths, and avoid obstacles using its stereo hazard and navigation cameras. It does so while continuously tracking a target of interest selected from 10 20 m away. The rover drives and tracks the target until it reaches the vicinity of the target. The rover then positions itself to approach the target, deploys its robotic arm, and places the end effector instrument on the designated target to within 2-3-cm accuracy of the originally selected target. This software features continuous navigation in a fairly rocky field in an outdoor environment and the ability to enable the rover to avoid large rocks and traverse over smaller ones. Using point-and-click mouse commands, a scientist designates targets in the initial imagery acquired from the rover s mast cameras. The navigation software uses stereo imaging, traversability analysis, path planning, trajectory generation, and trajectory execution. It also includes visual target tracking of a designated target selected from 10 m away while continuously navigating the rocky terrain. Improvements in this design include steering while driving, which uses continuous curvature paths. There are also several improvements to the traversability analyzer, including improved data fusion of traversability maps that result from pose estimation uncertainties, dealing with boundary effects to enable tighter maneuvers, and handling a wider range of obstacles. This work advances what has been previously developed and integrated on the Mars Exploration Rovers by using algorithms that are capable of traversing more rock-dense terrains, enabling tight, thread-the-needle maneuvers. These algorithms were integrated on the newly refurbished Athena Mars research rover, and were fielded in the JPL Mars Yard. Forty-three runs were conducted with targets at distances ranging from 5 to 15 m, and a success rate of 93% was achieved for placement of the instrument within 2-3 cm of the target

Forecasting financials and discovering menu prices with alternative data

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019Cataloged from PDF version of thesis.Includes bibliographical references (pages 101-105).In the financial industry, key quantities like public-company financials and consumer spending drive asset pricing and other decisions. However, direct observation of any company's financials or other key signals is rare. For instance, although public companies disclose their financials through quarterly reports and press releases, disclosures are infrequent and of limited information. This has led to an explosion in demand for "alternative" datasets: noisy, secondary signals of fine-grained company financials. Alternative datasets -- e.g. consumer credit card transactions --are increasingly available; however, quantitative methods for utilizing such noisy proxy signals are lacking. In this work, we develop quantitative methods for utilizing alternative data. Starting with datasets of anonymized consumer transactions, we focus on two problems: (i) forecasting and tracking company financials and (ii) estimating the prices customers pay for individual goods, and in what quantity. That is, first we estimate aggregate company financials (e.g. quarterly revenue) before zooming in to study customer spending details. Utilizing a novel forecasting and estimation framework, we outperform a standard Wall Street consensus benchmark in forecasting the quarterly financials of 34 public companies. Next, we perform seemingly counterintuitive inference: given an anonymous consumer's bill total (a single number), we estimate the number and prices of products purchased.We show implications in (i) detecting changes in product offerings and (ii) performing revenue attribution by product. To forecast and track company financials, we utilize a classical linear systems model to capture both the evolution of the hidden or latent state (e.g. daily revenue), as well as the proxy signal (e.g. credit cards transactions). We analytically solve the often irresolvable system identification problem, and provide a finite-sample analysis of the resulting error. We show this enables optimal inference with respect to mean-squared error. Last, we provide a novel, robust estimation algorithm for decomposing bill totals into the underlying, individual product(s) purchases. We prove correctness and accuracy under mild assumptions.by Michael Fleder.Ph. D.Ph.D. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Scienc