About

Zak Kingston is a Ph.D. student in Computer Science at Rice University, working with Dr. Lydia Kavraki. He graduated with a B.S. in Computer Science from Rice in Spring 2016 and his M.S. in Computer Science from Rice in December 2017. He is currently funded by a NASA NSTRF fellowship, working with the Robonaut 2 team. His research interests lie in algorithmic robotics, focusing on robot manipulation planning and planning with constraints.

News

Constrained Planning in OMPL

The generic constrained planning framework presented in ISRR 2017 is now available in OMPL! Take a look at the overview and the tutorial.


Research & Experience

Kavraki Lab @ Rice University

ZK is currently Ph.D. student in the Kavraki Robotics Lab at Rice University, working on robot motion planning with geometric constraints.

Dexterous Robotics Lab @ NASA JSC
ZK is an NSTRF Fellow with NASA Johnson Space Center's Dexterous Robotics Lab. He previously developed the motion planning system for NASA’s humanoid robot Robonaut 2.
MRSL @ Rice University
ZK used to work in the Multi-Robot Systems Lab (MRSL) at Rice University, where he worked on multi-robot manipulation.

Publications

Refereed Journal Articles

2018

[0]

An Incremental Constraint-Based Framework for Task and Motion Planning
Neil T. Dantam, Zachary Kingston, Swarat Chaudhuri, and Lydia E. Kavraki

Abstract / Bibtex / PDF / Publisher

We present a new algorithm for task and motion planning (TMP) and discuss the requirements and abstrations necessary to obtain robust solutions for TMP in general. Our Iteratively Deepened Task and Motion Planning (IDTMP) method is probabilistically-complete and offers improved performance and generality compared to a similar, state-of-the-art, probabilistically-complete planner. The key idea of IDTMP is to leverage incremental constraint solving to efficiently add and remove constraints on motion feasibility at the task level. We validate IDTMP on a physical manipulator and evaluate scalability on scenarios with many objects and long plans, showing order-of-magnitude gains compared to the benchmark planner and a four-times self-comparison speedup from our extensions. Finally, in addition to describing a new method for TMP and its implementation on a physical robot, we also put forward requirements and abstractions for the development of similar planners in the future.


Close


@article{Dantam2018,
 abstract = {We present a new algorithm for task and motion planning (TMP) and discuss the
requirements and abstrations necessary to obtain robust solutions for TMP in general. Our
Iteratively Deepened Task and Motion Planning (IDTMP) method is probabilistically-complete and
offers improved performance and generality compared to a similar, state-of-the-art,
probabilistically-complete planner. The key idea of IDTMP is to leverage incremental
constraint solving to efficiently add and remove constraints on motion feasibility at the task
level. We validate IDTMP on a physical manipulator and evaluate scalability on scenarios with
many objects and long plans, showing order-of-magnitude gains compared to the benchmark
planner and a four-times self-comparison speedup from our extensions. Finally, in addition to
describing a new method for TMP and its implementation on a physical robot, we also put
forward requirements and abstractions for the development of similar planners in the future.},
 author = {Neil T. Dantam and Zachary Kingston and Swarat Chaudhuri and Lydia E. Kavraki},
 doi = {10.1177/0278364918761570},
 journal = {International Journal of Robotics Research, Special Issue on the 2016 Robotics: Science and Systems Conference},
 title = {An Incremental Constraint-Based Framework for Task and Motion Planning},
 year = {2018}
}

Close


[1]

Sampling-Based Methods for Motion Planning with Constraints
Zachary Kingston, Mark Moll, and Lydia E. Kavraki

Abstract / Bibtex / PDF / Publisher

Robots with many degrees of freedom (e.g., humanoid robots and mobile manipulators) have increasingly been employed to accomplish realistic tasks in domains such as disaster relief, spacecraft logistics, and home caretaking. Finding feasible motions for these robots autonomously is essential for their operation. Sampling-based motion planning algorithms have been shown to be effective for these high-dimensional systems. However, incorporating task constraints (e.g., keeping a cup level, writing on a board) into the planning process introduces significant challenges. is survey describes the families of methods for sampling-based planning with constraints and places them on a spectrum delineated by their complexity. Constrained sampling-based methods are based upon two core primitive operations: (1) sampling constraint-satisfying configurations and (2) generating constraint-satisfying continuous motion. Although the basics of sampling-based planning are presented for contextual background, the survey focuses on the representation of constraints and sampling-based planners that incorporate constraints.


Close


@article{Kingston2018,
 abstract = {Robots with many degrees of freedom (e.g., humanoid robots and mobile manipulators)
have increasingly been employed to accomplish realistic tasks in domains such as disaster
relief, spacecraft logistics, and home caretaking. Finding feasible motions for these robots
autonomously is essential for their operation. Sampling-based motion planning algorithms have
been shown to be effective for these high-dimensional systems. However, incorporating task
constraints (e.g., keeping a cup level, writing on a board) into the planning process
introduces significant challenges. is survey describes the families of methods for
sampling-based planning with constraints and places them on a spectrum delineated by their
complexity. Constrained sampling-based methods are based upon two core primitive operations:
(1) sampling constraint-satisfying configurations and (2) generating constraint-satisfying
continuous motion. Although the basics of sampling-based planning are presented for contextual
background, the survey focuses on the representation of constraints and sampling-based
planners that incorporate constraints.},
 author = {Zachary Kingston and Mark Moll and Lydia E. Kavraki},
 doi = {10.1146/annurev-control-060117-105226},
 journal = {Annual Review of Control, Robotics, and Autonomous Systems},
 title = {Sampling-Based Methods for Motion Planning with Constraints},
 year = {2018}
}

Close


Refereed Conference Papers

2018

[0]

Distributed Object Characterization with Local Sensing by a Multi-Robot System
Golnaz Habibi, Sándor P. Fekete, Zachary Kingston, and James McLurkin

Abstract / Bibtex / PDF / Publisher / Video

This paper presents two distributed algorithms for enabling a swarm of robots with local sensing and local coordinates to estimate the dimensions and orientation of an unknown complex polygonal object, ie, its minimum and maximum width and its main axis. Our first approach is based on a robust heuristic of distributed Principal Component Analysis (DPCA), while the second is based on turning the idea of Rotating Calipers into a distributed algorithm (DRC). We simulate DRC and DPCA methods and test DPCA on real robots. The result show our algorithms successfully estimate the dimension and orientation of convex or concave objects with a reasonable error in the presence of noisy data.


Close


@incollection{Habibi2018,
 abstract = {This paper presents two distributed algorithms for enabling a swarm of robots with 
local sensing and local coordinates to estimate the dimensions and orientation of an 
unknown complex polygonal object, ie, its minimum and maximum width and its main axis. 
Our first approach is based on a robust heuristic of distributed Principal Component Analysis 
(DPCA), while the second is based on turning the idea of Rotating Calipers into a distributed 
algorithm (DRC). We simulate DRC and DPCA methods and test DPCA on real robots. The 
result show our algorithms successfully estimate the dimension and orientation of convex or 
concave objects with a reasonable error in the presence of noisy data.},
 author = {Golnaz Habibi and S{\'a}ndor P. Fekete and Zachary Kingston and James McLurkin},
 booktitle = {Distributed Autonomous Robotic Systems},
 doi = {10.1007/978-3-319-73008-0_15},
 editor = {Roderich Gro{\ss} and Andreas Kolling and Spring Berman and Emilio Frazzoli and Alcherio Martinoli and Fumitoshi Matsuno and Melvin Gauci},
 publisher = {Springer Proceedings in Advanced Robotics},
 title = {Distributed Object Characterization with Local Sensing by a Multi-Robot System},
 volume = {6},
 year = {2018}
}

Close


2017

[1]

Decoupling Constraints from Sampling-Based Planners
Zachary Kingston, Mark Moll, and Lydia E. Kavraki

Abstract / Bibtex / PDF / Video

We present a general unifying framework for sampling-based motion planning under kinematic task constraints which enables a broad class of planners to compute plans that satisfy a given constraint function that encodes, e.g., loop closure, balance, and end-effector constraints. The framework decouples a planner’s method for exploration from constraint satisfaction by representing the implicit configuration space defined by a constraint function. We emulate three constraint satisfaction methodologies from the literature, and demonstrate the framework with a range of planners utilizing these constraint methodologies. Our results show that the appropriate choice of constrained satisfaction methodology depends on many factors, e.g., the dimension of the configuration space and implicit constraint manifold, and number of obstacles. Furthermore, we show that novel combinations of planners and constraint satisfaction methodologies can be more effective than previous approaches. The framework is also easily extended for novel planners and constraint spaces.


Close


@inproceedings{Kingston2017,
 abstract = {We present a general unifying framework for sampling-based motion planning under
kinematic task constraints which enables a broad class of planners to compute plans that
satisfy a given constraint function that encodes, e.g., loop closure, balance, and
end-effector constraints. The framework decouples a planner’s method for exploration from
constraint satisfaction by representing the implicit configuration space defined by a
constraint function. We emulate three constraint satisfaction methodologies from the
literature, and demonstrate the framework with a range of planners utilizing these
constraint methodologies. Our results show that the appropriate choice of constrained
satisfaction methodology depends on many factors, e.g., the dimension of the configuration
space and implicit constraint manifold, and number of obstacles. Furthermore, we show that
novel combinations of planners and constraint satisfaction methodologies can be more
effective than previous approaches. The framework is also easily extended for novel planners
and constraint spaces.},
 address = {Puerto Varas, Chile},
 author = {Zachary Kingston and Mark Moll and Lydia E. Kavraki},
 booktitle = {International Symposium of Robotics Research},
 title = {Decoupling Constraints from Sampling-Based Planners},
 year = {2017}
}

Close


[2]

Robonaut 2 and You: Specifying and Executing Complex Operations
William Baker, Zachary Kingston, Mark Moll, Julia Badger, and Lydia E. Kavraki

Abstract / Bibtex / PDF / Publisher / Video

Crew time is a precious resource due to the expense of trained human operators in space. Efficient caretaker robots could lessen the manual labor load required by frequent vehicular and life support maintenance tasks, freeing astronaut time for scientific mission objectives. Humanoid robots can fluidly exist alongside human counterparts due to their form, but they are complex and high-dimensional platforms. This paper describes a system that human operators can use to maneuver Robonaut 2 (R2), a dexterous humanoid robot developed by NASA to research co-robotic applications. The system includes a specification of constraints used to describe operations, and the supporting planning framework that solves constrained problems on R2 at interactive speeds. The paper is developed in reference to an illustrative, typical example of an operation R2 performs to highlight the challenges inherent to the problems R2 must face. Finally, the interface and planner is validated through a case-study using the guiding example on the physical robot in a simulated microgravity environment. This work reveals the complexity of employing humanoid caretaker robots and suggest solutions that are broadly applicable.


Close


@inproceedings{Baker2017,
 abstract = {Crew time is a precious resource due to the expense of trained human operators in
space. Efficient caretaker robots could lessen the manual labor load required by frequent
vehicular and life support maintenance tasks, freeing astronaut time for scientific mission
objectives. Humanoid robots can fluidly exist alongside human counterparts due to their
form, but they are complex and high-dimensional platforms. This paper describes a system
that human operators can use to maneuver Robonaut 2 (R2), a dexterous humanoid robot
developed by NASA to research co-robotic applications. The system includes a specification
of constraints used to describe operations, and the supporting planning framework that
solves constrained problems on R2 at interactive speeds. The paper is developed in reference
to an illustrative, typical example of an operation R2 performs to highlight the challenges
inherent to the problems R2 must face. Finally, the interface and planner is validated
through a case-study using the guiding example on the physical robot in a simulated
microgravity environment. This work reveals the complexity of employing humanoid caretaker
robots and suggest solutions that are broadly applicable.},
 address = {Austin, TX},
 author = {William Baker and Zachary Kingston and Mark Moll and Julia Badger and Lydia E. Kavraki},
 booktitle = {IEEE Workshop on Advanced Robotics and its Social Impacts},
 doi = {10.1109/ARSO.2017.8025204},
 title = {Robonaut 2 and You: Specifying and Executing Complex Operations},
 year = {2017}
}

Close


2016

[3]

Incremental Task and Motion Planning: A Constraint-Based Approach
Neil T. Dantam, Zachary Kingston, Swarat Chaudhuri, and Lydia E. Kavraki

Abstract / Bibtex / PDF / Publisher / Video

We present a new algorithm for task and motion planning (TMP) and discuss the requirements and abstractions necessary to obtain robust solutions for TMP in general. Our Iteratively Deepened Task and Motion Planning (IDTMP) method is probabilistically-complete and offers improved performance and generality compared to a similar, state-of-the-art, probabilistically-complete planner. The key idea of IDTMP is to leverage incremental constraint solving to efficiently add and remove constraints on motion feasibility at the task level. We validate IDTMP on a physical manipulator and evaluate scalability on scenarios with many objects and long plans, showing order-of-magnitude gains compared to the benchmark planner and a four-times self-comparison speedup from our extensions. Finally, in addition to describing a new method for TMP and its implementation on a physical robot, we also put forward requirements and abstractions for the development of similar planners in the future.


Close


@inproceedings{Dantam2016,
 abstract = {We present a new algorithm for task and motion planning (TMP) and discuss the
requirements and abstractions necessary to obtain robust solutions for TMP in general. Our
Iteratively Deepened Task and Motion Planning (IDTMP) method is probabilistically-complete
and offers improved performance and generality compared to a similar, state-of-the-art,
probabilistically-complete planner. The key idea of IDTMP is to leverage incremental
constraint solving to efficiently add and remove constraints on motion feasibility at the
task level. We validate IDTMP on a physical manipulator and evaluate scalability on
scenarios with many objects and long plans, showing order-of-magnitude gains compared to the
benchmark planner and a four-times self-comparison speedup from our extensions. Finally, in
addition to describing a new method for TMP and its implementation on a physical robot, we
also put forward requirements and abstractions for the development of similar planners in
the future.},
 address = {Ann Arbor, MI},
 author = {Neil T. Dantam and Zachary Kingston and Swarat Chaudhuri and Lydia E. Kavraki},
 booktitle = {Robotics: Science and Systems},
 doi = {10.15607/RSS.2016.XII.002},
 title = {Incremental Task and Motion Planning: A Constraint-Based Approach},
 year = {2016}
}

Close


2015

[4]

Kinematically Constrained Workspace Control via Linear Optimization
Zachary Kingston, Neil T. Dantam, and Lydia E. Kavraki

Abstract / Bibtex / PDF / Publisher / Video

We present a method for Cartesian workspace control of a robot manipulator that enforces joint-level acceleration, velocity, and position constraints using linear optimization. This method is robust to kinematic singularities. On redundant manipulators, we avoid poor configurations near joint limits by including a maximum permissible velocity term to center each joint within its limits. Compared to the baseline Jacobian damped least-squares method of workspace control, this new approach honors kinematic limits, ensuring physically realizable control inputs and providing smoother motion of the robot. We demonstrate our method on simulated redundant and non-redundant manipulators and implement it on the physical 7-degree-of-freedom Baxter manipulator. We provide our control software under a permissive license.


Close


@inproceedings{Kingston2015,
 abstract = {We present a method for Cartesian workspace control of a robot manipulator that
enforces joint-level acceleration, velocity, and position constraints using linear
optimization. This method is robust to kinematic singularities. On redundant manipulators,
we avoid poor configurations near joint limits by including a maximum permissible velocity
term to center each joint within its limits. Compared to the baseline Jacobian damped
least-squares method of workspace control, this new approach honors kinematic limits,
ensuring physically realizable control inputs and providing smoother motion of the robot. We
demonstrate our method on simulated redundant and non-redundant manipulators and implement
it on the physical 7-degree-of-freedom Baxter manipulator. We provide our control software
under a permissive license.},
 author = {Zachary Kingston and Neil T. Dantam and Lydia E. Kavraki},
 booktitle = {IEEE-RAS International Conference on Humanoid Robots},
 doi = {10.1109/HUMANOIDS.2015.7363455},
 pages = {758--764},
 title = {Kinematically Constrained Workspace Control via Linear Optimization},
 year = {2015}
}

Close


[5]

Pipelined Consensus for Global State Estimation in Multi-Agent Systems
Golnaz Habibi, Zachary Kingston, Zijian Wang, Mac Schwager, and James McLurkin

Abstract / Bibtex / PDF / Publisher

This paper presents pipelined consensus, an extension of pair-wise gossip-based consensus, for multi-agent systems using mesh networks. Each agent starts a new consensus in each round of gossiping, and stores the intermediate results for the previous k consensus in a pipeline message. After k rounds of gossiping, the results of the first consensus are ready. The pipeline keeps each consensus independent, so any errors only persist for k rounds. This makes pipelined consensus robust to many real-world problems that other algorithms cannot handle, including message loss, changes in network topology, sensor variance, and changes in agent population. The algorithm is fully distributed and self-stabilizing, and uses a communication message of fixed size. We demonstrate the efficiency of pipelined consensus in two scenarios: computing mean sensor values in a distributed sensor network, and computing a centroid estimate in a multi-robot system. We provide extensive simulation results, and real-world experiments with up to 24 agents. The algorithm produces accurate results, and handles all of the disturbances mentioned above.


Close


@inproceedings{Habibi2015a,
 abstract = {This paper presents pipelined consensus, an extension of pair-wise gossip-based
consensus, for multi-agent systems using mesh networks. Each agent starts a new consensus in
each round of gossiping, and stores the intermediate results for the previous k consensus in a
pipeline message. After k rounds of gossiping, the results of the first consensus are ready.
The pipeline keeps each consensus independent, so any errors only persist for k rounds. This
makes pipelined consensus robust to many real-world problems that other algorithms cannot
handle, including message loss, changes in network topology, sensor variance, and changes in
agent population. The algorithm is fully distributed and self-stabilizing, and uses a
communication message of fixed size. We demonstrate the efficiency of pipelined consensus in
two scenarios: computing mean sensor values in a distributed sensor network, and computing a
centroid estimate in a multi-robot system. We provide extensive simulation results, and
real-world experiments with up to 24 agents. The algorithm produces accurate results, and
handles all of the disturbances mentioned above.},
 author = {Golnaz Habibi and Zachary Kingston and Zijian Wang and Mac Schwager and James McLurkin},
 booktitle = {International Conference on Autonomous Agents and Multiagent Systems},
 title = {Pipelined Consensus for Global State Estimation in Multi-Agent Systems},
 url = {http://dl.acm.org/citation.cfm?id=2772879.2773320},
 year = {2015}
}

Close


[6]

Distributed Centroid Estimation and Motion Controllers for Collective Transport by Multi-Robot Systems
Golnaz Habibi, Zachary Kingston, William Xie, Mathew Jellins, and James McLurkin

Abstract / Bibtex / PDF / Publisher / Video

This paper presents four distributed motion controllers to enable a group of robots to collectively transport an object towards a guide robot. These controllers include: rotation around a pivot robot, rotation in-place around an estimated centroid of the object, translation, and a combined motion of rotation and translation in which each manipulating robot follows a trochoid path. Three of these controllers require an estimate of the centroid of the object, to use as the axis of rotation. Assuming the object is surrounded by manipulator robots, we approximate the centroid of the object by measuring the centroid of the manipulating robots. Our algorithms and controllers are fully distributed and robust to changes in network topology, robot population, and sensor error. We tested all of the algorithms in real-world environments with 9 robots, and show that the error of the centroid estimation is low, and that all four controllers produce reliable motion of the object.


Close


@inproceedings{Habibi2015b,
 abstract = {This paper presents four distributed motion controllers to enable a group of robots to
collectively transport an object towards a guide robot. These controllers include: rotation
around a pivot robot, rotation in-place around an estimated centroid of the object,
translation, and a combined motion of rotation and translation in which each manipulating
robot follows a trochoid path. Three of these controllers require an estimate of the centroid
of the object, to use as the axis of rotation. Assuming the object is surrounded by
manipulator robots, we approximate the centroid of the object by measuring the centroid of the
manipulating robots. Our algorithms and controllers are fully distributed and robust to
changes in network topology, robot population, and sensor error. We tested all of the
algorithms in real-world environments with 9 robots, and show that the error of the centroid
estimation is low, and that all four controllers produce reliable motion of the object.},
 author = {Golnaz Habibi and Zachary Kingston and William Xie and Mathew Jellins and James McLurkin},
 booktitle = {IEEE International Conference on Robotics and Automation},
 doi = {10.1109/ICRA.2015.7139356},
 title = {Distributed Centroid Estimation and Motion Controllers for Collective Transport by Multi-Robot Systems},
 year = {2015}
}

Close


Theses

2017

[0]

A Unifying Framework for Constrained Sampling-Based Planning
Zachary Kingston

Abstract / Bibtex / PDF

Complex robots with many degrees-of-freedom (e.g., humanoids, mobile manipulators) have been increasingly applied to achieve tasks in fields such as disaster relief or spacecraft logistics. Finding motions for these systems autonomously is necessary if they are to be applied in unstructured environments not known a priori, as they must compute motions on-the-fly. Sampling-based motion planning algorithms have been shown to be effective for finding motions for high-dimensional systems such as these. However, the problems these robots face typically take the form of tasks with constraints, which limit the valid motions a robot can take (e.g., turning a valve about its axis, carrying a tray with both arms, etc.). Incorporating constraints while planning introduces significant challenges, as constraints induce a lower-dimensional manifold of constraint-satisfying configurations within the robot's configuration space. The lower-dimensional structure of the manifold throws a wrench into the basic operation of a sampling-based planner, necessitating a constraint methodology to provide a means for the planner to satisfy constraints. Within the literature, many constrained sampling-based motion planning methods have been proposed for sampling-based planning with constraints. Each of these methods introduces a constraint methodology of their own to tackle the issues raised when considering constraints. This thesis organizes several previously proposed constraint methodologies along of a spectrum, cataloged by the amount of bookkeeping and computation used to approximate the manifold of constraint-satisfying configurations. Notably, previous constrained sampling-based methods augment a single sampling-based algorithm with their constraint methodology to create a bespoke planner. This thesis presents a general framework for sampling-based motion planning with geometric constraints, unifying prior works by approaching the constrained motion planning problem at a higher level of abstraction. The framework decouples the constraint methodology from the planner's method for exploration by presenting the constraint-induced manifold as a configuration space to the planner, hiding details of the constraint methodology behind the space's primitive operations. Three constraint methodologies from the literature are emulated within the framework. The framework is demonstrated with a range of planners using the three emulated constraint methodologies in a set of simulated problems. Results show the advantages decoupling brings to constrained sampling-based planning, with novel combinations of planners and constraint methodologies surpassing emulated prior works. The framework is also easily extended for novel planners and constraint spaces.


Close


@mastersthesis{Kingston2017MS,
 abstract = {Complex robots with many degrees-of-freedom (e.g., humanoids, mobile manipulators)
have been increasingly applied to achieve tasks in fields such as disaster relief or
spacecraft logistics. Finding motions for these systems autonomously is necessary if they are
to be applied in unstructured environments not known a priori, as they must compute motions
on-the-fly. Sampling-based motion planning algorithms have been shown to be effective for
finding motions for high-dimensional systems such as these. However, the problems these
robots face typically take the form of tasks with constraints, which limit the valid motions
a robot can take (e.g., turning a valve about its axis, carrying a tray with both arms,
etc.). Incorporating constraints while planning introduces significant challenges, as
constraints induce a lower-dimensional manifold of constraint-satisfying configurations
within the robot's configuration space. The lower-dimensional structure of the manifold
throws a wrench into the basic operation of a sampling-based planner, necessitating a
constraint methodology to provide a means for the planner to satisfy constraints.
Within the literature, many constrained sampling-based motion planning methods have been
proposed for sampling-based planning with constraints. Each of these methods introduces a
constraint methodology of their own to tackle the issues raised when considering constraints.
This thesis organizes several previously proposed constraint methodologies along of a
spectrum, cataloged by the amount of bookkeeping and computation used to approximate the
manifold of constraint-satisfying configurations. Notably, previous constrained
sampling-based methods augment a single sampling-based algorithm with their constraint
methodology to create a bespoke planner.
This thesis presents a general framework for sampling-based motion planning with geometric
constraints, unifying prior works by approaching the constrained motion planning problem at a
higher level of abstraction. The framework decouples the constraint methodology from the
planner's method for exploration by presenting the constraint-induced manifold as a
configuration space to the planner, hiding details of the constraint methodology behind the
space's primitive operations. Three constraint methodologies from the literature are emulated
within the framework. The framework is demonstrated with a range of planners using the three
emulated constraint methodologies in a set of simulated problems. Results show the advantages
decoupling brings to constrained sampling-based planning, with novel combinations of planners
and constraint methodologies surpassing emulated prior works. The framework is also easily
extended for novel planners and constraint spaces.},
 address = {Houston, TX},
 author = {Zachary Kingston},
 school = {Rice University},
 title = {A Unifying Framework for Constrained Sampling-Based Planning},
 year = {2017}
}

Close



Teaching

COMP 450/550

ZK is a TA for COMP/ELEC/MECH 450/550, Algorithmic Robotics, in the Fall 2016, 2017, and 2018 semesters. COMP 450 is an introductory robotics course covering a broad selection of topics in modern robotics.

COMP 321

ZK was an in-lab TA for COMP 321, Introduction to Computer Systems, in the Spring 2015 and 2018 semesters. COMP 321 uses the C programming language to teach modern computer systems.

COMP 140

ZK was an in-class TA for COMP 140, Introduction to Computational Thinking, in Fall 2015. COMP 140 is a flipped classroom course, with lectures given through videos and class-time spent on hands-on exercises.

ENGI 128

ZK was a TA for ENGI 128, Introduction to Engineering Systems, in the Fall 2014 Semester. ENGI 128 is a freshman-only introduction to concepts in mechanical engineering, electrical engineering, and computer science. The course used the r-one robot, developed by the MRSL.


Service & Leadership

CAPC Consultant

ZK is a consultant for Rice's Center for Academic and Profession Communication.

CS GSA

ZK is the current financial directory for Rice's Computer Science Graduate Student Association. He is also the CS Department Representative for Rice's Graduate Student Association.

Reviewer for ICRA, ISRR

Outreach

Invited Talk at NASA JSC

ZK gave a talk at the Humanoid Users Symposium at NASA JSC about his work on Robonaut 2.

MANA de Tejas Gulf Coast

ZK gave an presentation on the basics of robotics with Dr. Mark Moll to MANA, a national Latina organization. Read about it in the Rice at Large publication.

Chicago MSI

ZK was a consultant with Dr. James McLurkin for the Chicago Museum of Science and Industry (MSI). The Robot Revolution Exhibit at MSI hosted the r-one robot in an interactive exhibit, where visitors could use a joystick to command a swarm.

Summer Swarm Camp

ZK was a TA for the Summer Swarm Robot Camp hosted by the MRSL. The camp was designed for middle school and high school students in the community interested in robotics and computer science to get a hands-on experience working with a robot and programming its behavior. The camp used the r-one robot, and the Python programming language.


Awards

NSTRF

ZK accepted the NASA Space Technology Research Fellowship. You can read about it on NASA's page! Read about it in the Rice Engineering News.

NSF GRFP

ZK was awarded the National Science Foundation's Graduate Research Fellowship. Read about it in the Rice Computer Science News. There was also an interview.

CS GRF

ZK was awarded the Graduate Research Fellowship for Rice Undergraduates by the Rice Computer Science Department.

Distinction in Research

ZK was awarded the Distinction in Research and Creative Works, a university honor.


Miscellaenous

Coroga

An experiment with three.js to procedurally generate aesthetically pleasing rock gardens. Check it out! Code is on Github.

Bassist Functions

In his spare time, ZK plays in a band with some friends from applied mathematics. Check them out on SoundCloud!

A picture of me from Spring 2016.

Contact

zakrice.edu
Duncan Hall 3052