Ph.D. in Robotics | Full Program of Study

The main emphasis of the Ph.D. program is the successful completion of an original and independent research thesis. The degree requirements are designed around this goal.

Minimum Requirements

  • Completion of 36 semester hours of courses with a letter grade
  • Passing a comprehensive qualifying exam with written and oral components.
  • Successfully conducting, documenting, and defending a piece of original research culminating in a doctoral thesis.


Home Unit Teaching Apprenticeship and Extra-curricular Requirements Robotics Ph.D. students are subject to their home unit's teaching apprenticeship requirements (e.g., a certain number of semesters serving as a TA) and other the extra-curricular requirements such as seminar attendance or annual review process. For example, students with home units in BME, IC, and ME are required to do two semesters of teaching practicum or apprenticeship and register for the corresponding courses. Students should contact their home units for details for any departmental requirements that are in addition to the Robotics degree requirements. Students are responsible for ensuring that they understand and satisfy any home unit requirements as well as the Robotics program and Institute requirements.


Ph.D. Robotics Degree Requirements – 36 semester hours with a letter grade

Component Courses Hours Required
Intro to Robotics Research  CS/AE/ECE/ME 7785, Introduction to Robotics Research.  3
Foundation Courses Three foundation courses, each selected from distinct core areas: Mechanics, Controls, Perception, Artificial Intelligence, and Human-Robot Interaction (HRI). 9
Elective Courses Three targeted elective courses, each selected from the same three core areas used for the foundation courses.  9
Multidisciplinary Robotics Research Two new courses CS/AE/ECE/ME 8750 and CS/AE/ECE/ME 8751, Multidisciplinary Robotics Research I and II.  6
Courses Outside the Major Three courses outside the major area to provide a coherent minor in accordance with Institute policies.  9
  TOTAL 36

*A maximum of two classes (6 semester hours) at the 4000 level may be used to satisfy the minor requirements only. No courses used to satisfy any bachelor's degree requirements can be used towards a graduate degree.

Ph.D. Candidacy

Prior to completing all of these requirements, Georgia Tech defines the Ph.D. Candidate milestones. Admission to candidacy requires that the student:

  1. Complete all course requirements (except the minor);
  2. Achieve a satisfactory scholastic record;
  3. Pass the comprehensive examination;
  4. Submit and receive approval naming the dissertation topic and delineating the research topic.

(Georgia Institute of Technology 2006-2007 General Catalog, p. 122)

Core Area Courses

The following courses are in the robotics core areas of Mechanics, Control, Perception, Artificial Intelligence, and Human-Robot Interaction (HRI). They are used to select three foundation courses and three targeted elective courses.

Foundation courses are marked by an asterisk (*).

Component Courses

Students may take two core courses – one in Robotics (BME 8813 or ME 6407) and one in Dynamics (AE 6210 or ME 6441) and may use the second core class in place of a mechanics elective course.

  • AE 6210*, Advanced Dynamics I – Kinematics of particles and rigid bodies, angular velocity, inertia properties, holonomic and nonholonomic constraints, generalized forces. Prerequisite: AE 2220. 3 credit hours
  • AE 6211, Advanced Dynamics II – A continuation of AE 6210. Equations of motion, Newtonian frames, consistent linearization, energy and momentum integrals, collisions, mathematical representation of finite rotation. Prerequisite: AE 6210. 3 credit hours
  • AE 6230, Structural Dynamics – Dynamic response of single-degree-of-freedom systems, Lagrange's equations; modal decoupling; vibration of Euler-Bernoulli and Timoshenko beams, membranes and plates. Prerequisites: AE 3120, AE 3515. 3 credit hours
  • AE 6263, Flexible Multi-Body Dynamics – Nonlinear, flexible multi-body dynamic systems, parameterization of finite rotations, strategies for enforcement of holonomic and non holonomic constraints, formulation of geometrically nonlinear structural elements, time-integration techniques. Prerequisites: AE 6211, AE 6230. 3 credit hours
  • AE 6270, Nonlinear Dynamics – Nonlinear vibration methods through averaging and multiple scales, bifurcation, periodic and quasi-periodic systems, transition to chaos, characterization of chaotic vibrations, thermodynamics of chaos, chaos control. Prerequisite: AE 6230. 3 credit hours
  • AE 6520, Advanced Flight Dynamics — Reference frames and transformations, general equations of unsteady motion, application to fixed-wing, rotary-wing and space vehicles, stability characteristics, flight in turbulent atmosphere. 3 credit hours
  • BMED 8813*, Robotics — Robot kinematics, statics, and dynamics. Open-chain manipulators and parallel manipulators as well as an understanding of trajectory planning and non-holonomic systems. 3 credit hours
  • CS 7496, Computer Animation — Motion techniques for computer animation and interactive games (keyframing, procedural methods, motion capture, and simulation) and principles for storytelling, composition, lighting, and interactivity. 3 credit hours
  • ME 6705, Introduction to Mechatronics – Modeling and control of actuators and electro-mechanical systems. Performance and application of microprocessors and analog electronics to modern mechatronic systems. Prerequisites ME 3015 or equivalent, or with the consent of the instructor. 4 credit hours
  • ME 6407*, Robotics – Analysis and design of robotic systems including arms and vehicles. Kinematics and dynamics. Algorithms for describing, planning, commanding and controlling motion force. Prerequisites ME 3015 or ECE 3085. 3 credit hours
  • ME 6441*, Dynamics of Mechanical Systems – Motion analysis and dynamics modeling of systems of particles and rigid bodies in three-dimensional motion. Prerequisites: ME 3015 or equivalent, or with the consent of the instructor. 3 credit hours
  • ME 6442, Vibration of Mechanical Systems – Introduction to modeling and oscillatory response analysis for discrete and continuous mechanical and structural systems. Prerequisites: ME 3015 and ME 3201. 3 credit hours
  • ME 7442, Vibration of Continuous Systems – Equations of motion and oscillatory response of dynamic systems modeled as continuous media. Prerequisites: ME 6442 or equivalent, or with the consent of the instructor. 3 credit hours
  • AE 6252, Smart Structure Control – Modeling smart sensors and actuators, development of closed loop models, design of controllers, validation of controllers, application to vibration control, noise control, and shape control. Prerequisite: AE 6230. 3 credit hours
  • AE 6504, Modern Methods of Flight Control – Linear quadratic regulator design. Model following control. Stochastic control. Fixed structure controller design. Applications to aircraft flight control. Prerequisite: AE 3521. 3 credit hours
  • AE 6505, Kalman Filtering – Probability and random variables and processes; correlation; shaping filters; simulation of sensor errors; Wiener filter; random vectors; covariance propagation; recursive least-squares; Kalman filter; extensions. Prerequisite: AE 3515. 3 credit hours
  • AE 6506, Guidance and Navigation – Earth's shape and gravity. Introduction to inertial navigation. GPS aiding. Error analysis. Guidance systems. Analysis of the guidance loop. Estimation of guidance variables. Adjoint analysis. Prerequisite: AE 3521. 3 credit hours
  • AE 6511, Optimal Guidance and Control – Euler-Lagrange formulation; Hamilton-Jacobi approach; Pontryagin's minimum principle; Systems with quadratic performance index; Second variation and neighboring extremals; Singular solutions; numerical solution techniques. Prerequisite: AE 3515. 3 credit hours
  • AE 6530*, Techniques for analysis and description of multivariable linear systems. Tools for advanced feedback control design for these systems, including computational packages. Credit will not be awarded for both AE 6530 and ECE 6550 or AE 6530 and ME 6401.  3 credit hours.
  • AE 6531, Robust Control I – Robustness issues in controller analysis and design. LQ analysis, H2 norm, LQR, LQG, uncertainty modeling, small gain theorem, H-infinity performance, and the mixed-norm H2/H-infinity problem. Prerequisite: ECE 6550. 3 credit hours
  • AE 6532, Robust Control II – Advanced treatment of robustness issues. Controller analysis and design for linear and nonlinear systems with structured and non-structured uncertainty. Reduced-order control, stability, multipliers, and mixed-mu. Prerequisite: ECE 6531. 3 credit hours
  • AE 6534, Control of AE Structures – Advanced treatment of control of flexible structures. Topics include stability of multi-degree-of-freedom systems, passive and active absorbers and isolation, positive real models, and robust control for flexible structures. Prerequisite: ECE 6230, ECE 6531. 3 credit hours
  • AE 6580, Nonlinear Control – Advanced treatment of nonlinear robust control. Lyapunov stability theory, absolute stability, dissipativity, feedback linearization, Hamilton-Jacobi-Bellman theory, nonlinear H-infinity, backstepping control, and control Lyapunov functions. Prerequisite: ECE 6550. 3 credit hours
  • AE 8803 Nonlinear Stochastic Optimal Control 3 credit hours
  • ECE 6550*, Linear Systems and Controls – Introduction to linear system theory and feedback control. Topics include state space representations, controllability and observability, linear feedback control. Prerequisite: Graduate Standing. 3 credit hours
  • ECE 6551, Digital Controls – Techniques for analysis and synthesis of computer-based control systems. Design projects provide an understanding of the application of digital control to physical systems. Prerequisites: ECE 6550 Minimum Grade of D. 3 credit hours
  • ECE 6552, Nonlinear Systems and Control – Classical analysis techniques and stability theory for nonlinear systems. Control design for nonlinear systems, including robotic systems. Includes design projects. Prerequisites: ECE 6550 Minimum Grade of D. 3 credit hours
  • ECE 6553, Optimal Control and Optimization – Optimal control of dynamic systems, numerical optimization, techniques and their applications in solving optical-trajectory problems. Prerequisites: ECE 6550 Minimum Grade of D. 3 credit hours
  • ECE 6554, Adaptive Control – Methods of parameter estimation and adaptive control for systems with constant or slowly varying unknown parameters. Includes MATLAB design projects emphasizing applications to physical systems. Prerequisites: ECE 6550 Minimum Grade of D. 3 credit hours
  • ECE 6555, Optimal Estimation – Techniques for signal and state estimation in the presence of measurement and process noise with the emphasis on Wiener and Kalman filtering. Prerequisites: ECE 6550 Minimum Grade of D. 3 credit hours
  • ECE 6559, Advanced Linear Systems – Study of multivariable linear system theory and robust control design methodologies. Prerequisites: ECE 6550 Minimum Grade of D. 3 credit hours
  • ECE 6563 Networked Control and Multiagent Systems
  • ME 6401*, Linear Control Systems – Theory and applications of linear systems, state space, stability, feedback controls, observers, LQR, LQG, Kalman Filters. Prerequisite: ME 3015 or equivalent, or with the consent of the instructor. 3 credit hours
  • ME 6402, Nonlinear Control Systems – Analysis of nonlinear systems, geometric control, variable structure control, adaptive control, optimal control, applications. Prerequisite: ME 6401 or equivalent, or with the consent of the instructor. 3 credit hours
  • ME 6403, Digital Control Systems – Comprehensive treatment of the representation, analysis, and design of discrete-time systems. Techniques include Z- and W- transforms, direct method, control design, and digital tracking. Prerequisite: ME 3015 or equivalent, or with the consent of the instructor. 3 credit hours
  • ME 6404, Advanced Control System Design and Implementation – Analysis, synthesis and implementation techniques of continuous-time and real-time control systems using classical and state-space methods. Prerequisite: ME 6403 or equivalent, or with the consent of the instructor. 3 credit hours
  • CS 6476*, Computer Vision – Introduction to computer vision including fundamentals of image formation, camera imaging geometry, feature detection and matching, stereo, motion estimation and tracking, image classification and scene understanding. Credit not awarded for both CS 6476 and CS 4495 or CS 4476. Credit will not be awarded for both CS 6476 and ME 6406. 3 credit hours
  • CS 7476, Advanced Computer Vision – Advanced topics in computer vision, which includes a deep dive into both the theoretical foundations of computer vision to the practical issues of building real systems that use computer vision. Credit not awarded for CS 7476 and CS 7495. 3 credit hours
  • CS 7616, Pattern Recognition – This course provides an introduction to the theory and practice of pattern recognition. It emphasizes unifying concepts and the analysis of real-world datasets. 3 credit hours
  • CS 7636, Computational Perception – Study of statistical and algorithmic methods for sensing people using video and audio. Topics include face detection and recognition, figure tracking, and audio-visual sensing. Prerequisites: CS 4641 and (CS 4495 or CS 7495) 3 credit hours
  • CS 7499, 3D Reconstruction and Mapping – Course focuses on multi-robot/multi-camera mapping and reconstruction. Topics range from SLAM, graphical model inferences, and understanding the practical issues regarding multi-platform reconstruction. 3 credit hours
  • CS 7626 Behavioral Imaging – Theory and methods for measuring, recognizing, and quantifying social and communicative behavior using video, audio, and wearable sensor data. 3 credit hours
  • CS 7643 Deep Learning, 3 credit hours
  • ECE 6255, Digital Processing of Speech Signals – The application of digital signal processing to problems in speech communication. Includes a laboratory project. Prerequisites: ECE 4270 Minimum Grade of D. 3 credit hours
  • ECE 6258, Digital Image Processing – An introduction to the theory of multidimensional signal processing and digital image processing, including key applications in multimedia products and services, and telecommunications. Prerequisites: ECE 4270 Minimum Grade of D. 3 credit hours
  • ECE 6273, Pattern Recognition – Theory and application of pattern recognition with a special application section for automatic speech recognition and related signal processing. Prerequisites: ECE 4270 Minimum Grade of D. 3 credit hours
  • ECE 6560, PDEs in Image Processing and Computer Vision – Mathematical foundations and numerical aspects of partial-differential equation techniques used in computer vision. Topics include image smoothing and enhancement, edge detection, morphology, and image reconstruction. Prerequisites: ECE 6550 Minimum Grade of D. 3 credit hours
  • ME 6406*, Machine Vision – Design of algorithms for vision systems for manufacturing, farming, construction, and the service industries. Image processing, optics, illumination, feature representation. Prerequisite: Graduate Standing in engineering or related discipline. Credit will not be awarded for both CS 6476 and ME 6406. 3 credit hours
Artificial Intelligence
  • CS 6601*, Artificial Intelligence – Basic concepts and methods of artificial intelligence including both symbolic/conceptual and numerical/probabilistic techniques. Prerequisites: CS 2600
  • CS 7612, AI Planning – Symbolic numerical techniques that allow intelligent systems to decide how they should act in order to achieve their goals, including action and plan representation, plan synthesis and reasoning, analysis of planning algorithms, plan execution and monitoring, plan reuse and learning, and applications. Prerequisites: CS 6601
  • CS 7640, Learning in Autonomous Agents – An in-depth look at agents that learn, including intelligent systems, robots, and humans. Design and implementation of computer models of learning and adaptation in autonomous intelligent agents. Prerequisites: CS 3600 or CS 4641
  • CS 7641 Machine Learning – Machine learning techniques and applications. Topics include foundational issues; inductive, analytical, numerical, and theoretical approaches; and real-world applications. Prerequisites: CS 6601 Credit not awarded for both CS 7641 and ME 8813.
  • CS 7643 Deep Learning, 3 credit hours
  • CS 7648 Interactive Robot Learning, This course combines lectures in CS (Machine and Reinforcement Learning) and CogSci with a research seminar to enable students to develop learning from demonstration systems, 3 credit hours.
  • CS 8803, Mobile Manipulation – The objective of the course is to gain knowledge of methods for design of mobile manipulation systems. The course covers all aspects of the problem from navigation and localization over kinematics and control to visual and force based perception.
  • CS 7649, Robot Intelligence Planning (previously CS 8803, Robot Intelligence: Planning in action) - Course covers methods for planning with symbolic, numerical, geometric and physical constraints. Topics will range from classical and stochastic planning to continuous robot domains and hybrid control of dynamic systems.
  • CS 8803, Computation and the Brain
  • CS 7462 Reinforcement Learning: Efficient algorithms for multiagent planning, and approaches to learning near-optimal decisions using possibly partially observable Markov decision processes; stochastic and repeated games; and reinforcement learning, 3 credit hours
  • CS 8803, Statistical Techniques in Robotics
  • CS 8803, Robot Motion Planning, The topic of this course is robot motion planning. This includes the geometric problem of computing collision-free paths for robots, the problem of planning paths for robots with nonholonomic constraints (e.g., wheeled mobile robots), and the problem of planning trajectories that take into consideration robot dynamics (e.g., locomotion or flight), 3 credit hours.
  • CS/ECE 7751, Graphic Models in ML (previously CS 8803/ECE 8803, Probabilistic Graph Models and ML in High Dimensions)
  • ECE 6254 Statistical Machine Learning: An introduction to the theory of statistical learning and practical machine learning algorithms with applications in signal processing and data analysis, 3 credit hours
  • ECE 6556, Intelligent Control – Principles of intelligent systems and their utility in modeling, identification, and control of complex systems; neuro-fuzzy tools applied to supervisory control; hands-on laboratory experience. Prerequisites: ECE 6550 Minimum Grade of D. 3 credit hours
  • ME 8813*, Machine Learning Fundamentals for Mechanical Engineering Students may take CS 6601 as the foundation course and ME 8813 as the elective. Credit not awarded for both CS 7641 and ME 8813.
Human-Robot Interaction (HRI)

HRI includes two core courses. Students are encouraged, but not required to take both HRI core courses. Students taking both core courses may use their second core class in place of an HRI elective course.

  • AE 6551,  Cognitive Engineering - Cognitive engineering addresses a range of technologies and work environments that will support human cognitive performance, including information systems, decision support, automation, and intelligent systems.
  • AE 6721*, Evaluation of Human Integrated Systems – Evaluation of human integrated systems including translating research questions into measurable objectives, overview of evaluation methods and data analysis techniques applicable to such systems. 3 credit hours
  • CS 7633*, Human-Robot Interaction – Survey of the state of the art in HRI research, introduction to statistical methods for HRI research, research project studio. A petition has been filed for this to be added to the permanent CS curriculum and have permanent course number. 3 credit hours
  • CS 6455, User Interface Design and Evaluation – Qualitative empirical methods for understanding human-technology interaction. 3 credit hours
  • CS 6750, Human-Computer Interact – Describes the characteristics of interaction between humans and computers and demonstrates techniques for the evaluation of user-centered systems. 3 credit hours
  • CS 7648 Interactive Robot Learning, This course combines lectures in CS (Machine and Reinforcement Learning) and CogSci with a research seminar to enable students to develop learning from demonstration systems, 3 credit hours.
  • CS 8803 Computational Social Robotics 3 credit hours
  • ISYE 6215, Human-Machine Systems – The development and use of mathematical models of human behavior are considered. Approaches from estimation theory, control theory, queuing theory, and fuzzy set theory are considered. 3 credit hours
  • PSYC 6011, Cognitive Psychology – Survey course on human cognition including pattern recognition, attention, memory, categorization, problem solving, consciousness, decision making, intention, and the relation between mind and brain.
  • PSYC 6014, Sensation & Perception – This course examines how sensations and perceptions of the outside world are processed by humans, including physiological, psychophysical, ecological, and computational perspectives. 3 credit hours
  • PSYC 6017, Human Abilities – Theory, methods, and applications of research on human abilities, including intelligence, aptitude, achievement, learning, aptitude treatment interactions, information processing correlates, and measurement issues. 3 credit hours
  • PSYC 7101, Engineering Psych I – Basic methods used to study human-machine systems including both system analysis and human performance evaluation techniques. These methods will be applied to specific systems. 3 credit hours
  • PSYC 7104, Psychomotor & Cog Skill – Human capabilities and limitations for learning and performing psychomotor and cognitive skills are studied. 3 credit hours
* Indicates foundation course

Required Fundamental Courses

Three required fundamental courses are designed specifically for the Robotics Ph.D.:

Course Details
Introduction to Robotics Research

Provides students with a familiarization of the core areas of robotics including Mechanics, Control, Perception, Artificial Intelligence, and Human Robot Interaction. Provides an introduction to the fundamental mathematical and computational tools required in robotics research. (3 credit hours).

The desired learning outcome is to provide a strong theoretical foundation for students on the multidisciplinary subject matters found in robotics. This is accomplished by:

  1. Providing an introduction to the fundamentals of robotics in the core areas of mechanics, control, perception, artificial intelligence, and Human Robot Interaction.
  2. Providing the basic theoretical and computational tools to support the core areas in robotics.
  3. The course will familiarize the students with a mobile robot platform based on the Robot Operating System (ROS). The students have access to the lab space housing the mobile robots around the clock.

Multidisciplinary Robotics Research I
(3 credit hours)
Prerequisite: CS/AE/ECE/ME 7785

Multidisciplinary Robotics Research II
(3 credit hours)
Prerequisite: CS/AE/ECE/ME 8750

These courses form a two semester sequence with similar “laboratory-rotation” formats. Each course requires the student to complete a semester-long research project under the guidance of at least two faculty members from distinct participating schools (AE, BME, CoC, ECE, or ME). The courses are designed to expose students to the discipline of research in a structured way and to encourage novel ideas in a multidisciplinary context.

The desired learning outcome is to foster a multidisciplinary research approach in the student by:

  • Critically assessing the prior art in an area outside his/her own,
  • Performing state-of-the-art experimental or simulation work in a multidisciplinary area,
  • Coherently reporting, at the level of a conference publication, on the research performed.

The evaluation component includes:

  • Week 2, a one page proposal outlining the proposed work along with a well-argued motivation;
  • Week 5: a detailed written review paper of the state-of-the art in the area;
  • Week 10: a written report on the experimental or simulation component in progress;
  • Week 14: a final report and presentation that includes all of the above along with a discussion of the work done and opportunities for future work.

All deliverables will be graded by both faculty advisors as well as reviewed to comply with the evaluation criteria set by the Robotics Program Committee.

Minor Field of Study

The Robotics Ph.D. Minor consists of three related courses (nine semester credit hours) outside of robotics that forms a coherent field of study in accordance with the Institute’s policies. The minor courses must be distinct from any of the robotics core areas (i.e., are not listed under any of the 5 core areas on this website) but can be taken from the student’s home school as long as they are distinct from robotics courses (e.g., ECE-ROBO student can take ECE circuits courses or ME students can take fluid mechanics courses).

Qualifying Exam

The purpose of the comprehensive exam is to:

  • Assess the student's general knowledge of the degree area
  • Assess the student's specialized knowledge of the chosen research area

The comprehensive examination provides an early assessment of the student's potential to satisfactorily complete the requirements for the doctoral degree. As such, it requires that fundamental principles be mastered and integrated so that they can be applied to solving problems relevant to robotics.


The Robotics Ph.D. qualifying exam has two components and the student is required to pass both to continue in the program:

  • Course-based GPA requirement
  • Comprehensive Oral Examination

To pass the course-based part, the student must maintain a GPA of 3.5 or higher in 4 courses taken at Georgia Tech from exactly 2 distinct core areas form the 5 core areas of robotics curriculum. Two of these courses must be foundation courses (1 course from each core area, say core area, C1 and core area, C2). The remaining two courses may be either elective or foundation with one course from the first core area, C1, and the second course from the second core area, C2. Two Foundation courses from the same core area are accepted only if credit is allowed for both courses simultaneously (i.e., only if they cover different subject areas). The student must complete the four courses for the GPA requirement by the end of the 6th semester (which includes summer semesters) of starting in the program.

The 2nd component of the ROBO qualifying exam is a comprehensive oral examination administered by an exam committee of at least three (3) Robotics faculty members. The committee must include the student’s primary advisor. Goals of the oral exam include the following:

  • Determine student’s ability to understand and apply fundamental concepts in the general area of Robotics
  • Determine the student’s ability to conduct independent research and review, synthesize, and evaluate previous work from the literature
  • Identify areas of weakness that the student may need to improve upon.

The student will prepare for the examination based on a specific research topic assigned by the exam committee in consultation with the student three weeks in advance. The first attempt for the comprehensive oral exam must be made before the end of the student’s 5th semester (which includes summer semesters) in the program. If the student fails the oral exam the first time, he/she is allowed only 1 re-take and passing of the exam in order to remain in the Ph.D. program. The re-take of the oral exam must be on the same general topic and be administered by the same Committee as the original exam barring any unforeseen or extraneous circumstances. The exam must be completed by the end of the 8th semester (which includes summer semesters) of starting in the Ph.D. program.

Appeals Process

If a student fails the oral exam on his/her second attempt, he/she has the right to appeal the decision to the Program director who will refer the matter to the Program faculty to confirm or override the outcome of the qualification examination process. The Program faculty may hear from only the voting-eligible student’s advisor and the Chair of the exam committee before reaching a decision of whether the student can remain in the program by secret ballot.

Ph.D. Thesis

The Ph.D. dissertation describes the results of a research project and demonstrates that the candidate possesses powers of original thought, talent for research, and ability to organize and present findings.

Dissertation Advisory Committee

The student presents and defends a written Ph.D. proposal to a Dissertation Advisory Committee of at least five faculty members approved by the Robotics Program Committee. The Dissertation Advisory Committee consists of five or more members where:

  • At least three members must be faculty affiliated with the Robotics Program or from the student's Home School (CoC, AE, BME, ECE, ME).
  • At least two members must be from outside of the student’s Home School

Ph.D. Dissertation Proposal

The objective of the Ph.D. Proposal is to allow an early assessment of your chosen topic of research for the satisfactory completion of the doctoral degree. The proposal should delineate your specific area of research by stating the purpose, scope, methodology, overall organization, and limitations of the proposed study area. The proposal must include a review of the relevant literature and indicate the expected contribution of the research.

The proposal should be organized as follows:

  • Summary - limited to 200 words.
  • Table of Contents
  • Project Description - a clear statement of the work to be undertaken. Limited to 15 pages single spaced (30 pages double spaced) and including all graphic elements and tables.
  • Bibliography

Pages should be of standard size (8½" x 11"; 21.6 cm x 27.9 cm) with minimum 1" or 2.5 cm margins at the top, bottom, and on each side. The minimum type font size is 10 to 12 points.

Dissertation Defense

The dissertation, when completed, must be publicly defended before an Examination Committee approved by the Graduate Studies office. In most instances the Examination Committee is expected to be the same as the Dissertation Advisory Committee. If a candidate should fail to pass the final oral examination, the Examining Committee may recommend permission for one additional examination. It is expected that the dissertation results will be published in peer-reviewed journals and conferences.

Details on preparing and submitting a dissertation according to institute guidelines are available on the Graduate Admissions website.

Residency Requirement

“Doctoral students must spend at least two full-time semesters in residence at the Georgia Institute of Technology and ordinarily must complete research for the dissertation while in residence.” (Georgia Tech 2009-10 General Catalog)