Hi everyone! I am Zachary Kingston, best known as Zak. I am currently a PhD student in the Computer Science program at Rice University, working under the direction of Dr. Lydia Kavraki. My research interest is in robotics, focused on solving the difficulties that arise in robotic manipulation. Read more down below.

I received my Bachelors of Science in Computer Science from Rice in Spring 2016 with a Distinction in Research and Creative Works. I was awarded the Graduate Research Fellowship for Rice Undergraduates in Spring 2015. I am currently working in the Kavraki Robotics Lab. I have worked in the Multi-Robot Systems Lab at Rice. I have been a TA for COMP 321: Introduction to Computer Systems, COMP 140: Introduction to Computational Thinking, and ENGI 128: Introduction to Engineering Systems. Read more down below.

A picture of me from Spring 2016.

Zak Kingston

"zak" rice.edu

Duncan Hall 3011

News and Recent Events

Houston Robotics and AI Day 2016/07/22

I was at Houston Robotics and AI Day! I gave a presentation and poster on LC3.

Article about Humanoids 2015 Paper 2015/11/13

My trip to Korea to present LC3 was mentioned in the Rice Press! Check it out!

Research and Projects


Linearly Constrained Cartesian Control

The Baxter robot by Rethink Robotics is a finicky beast, especially when attempting precise manipulation. So, I developed a Cartesian space controller that is fast and efficient to mitigate the problems with controlling Baxter effectively.

Multi-Robot Manipulation

Multi-Robot Manipulation

I collaborated with Dr. Golnaz Habibi during her graduate studies on research in fully distributed algorithms for multi-robot manipulation of planar objects.

Robot Communication Client GUI

r-one Utilties

I wrote some utility programs for communicating with the r-one robot while I was a member of the MRSL.



Incremental Task and Motion Planning: A Constraint-Based Approach

Neil Dantam, Zachary Kingston, Swarat Chaudhuri, Lydia Kavraki

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.
  author = {Dantam, Neil T. and Kingston, Zachary K. and Chaudhuri, Swarat and Kavraki, Lydia E.},
  title = {Incremental Task and Motion Planning: A Constraint-Based Approach},
  booktitle = {Robotics: Science and Systems},
  year = {2016},
  keywords = {planning from high-level specifications},
  url = {http://www.roboticsproceedings.org/rss12/p02.html}


Kinematically Constrained Workspace Control via Linear Optimization

Zachary Kingston, Neil Dantam, Lydia Kavraki

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 = {Kingston, Zachary and Dantam, Neil and Kavraki, Lydia},
  booktitle = {IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids)},
  title = {Kinematically constrained workspace control via linear optimization},
  year = {2015},
  pages = {758--764},
  doi = {10.1109/HUMANOIDS.2015.7363455},
  keywords = {other robotics}

Pipelined Consensus for Global State Estimation in Multi-Agent Systems

Golnaz Habibi, Zachary Kingston, Zijian Wang, Mac Schwager, James McLurkin

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 = {Habibi, Golnaz and Kingston, Zachary and Wang, Zijian and Schwager, Mac and McLurkin, James},
  title = {Pipelined Consensus for Global State Estimation in Multi-Agent Systems},
  booktitle = {Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems},
  series = {AAMAS '15},
  year = {2015},
  isbn = {978-1-4503-3413-6},
  location = {Istanbul, Turkey},
  pages = {1315--1323},
  numpages = {9},
  url = {http://dl.acm.org/citation.cfm?id=2772879.2773320},
  acmid = {2773320},
  publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
  address = {Richland, SC},
  keywords = {centroid estimation, communication failure, consensus, distributed, multi-robot},

Distributed Centroid Estimation and Motion Controllers for Collective Transport by Multi-Robot Systems

Golnaz Habibi, Zachary Kingston, William Xie, Mathew Jellins, James McLurkin

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={G. Habibi and Z. Kingston and W. Xie and M. Jellins and J. McLurkin}, 
  booktitle={2015 IEEE International Conference on Robotics and Automation (ICRA)}, 
  title={Distributed centroid estimation and motion controllers for collective transport by multi-robot systems}, 

Teaching and Outreach

MANA de Tejas Gulf Coast

I gave an presentation on the basics of robotics with Dr. Mark Moll to a group from MANA, a national Latina organization.


COMP 140

I was an in-class TA for COMP 140, Introduction to Computational Thinking, in the Fall 2015 semester. COMP 140 is unique as it is a flipped classroom, where lectures are given through videos outside of class, and class-time is spent with hands-on exercises. I guided 3-person teams throughout the entire semester through a gamut of topics in computer science.

COMP 321

I was an in-lab TA for COMP 321, Introduction to Computer Systems, in the Spring 2015 semester. COMP 321 in an introductory systems course, using the C programming language to impart deeper knowledge of how modern computer systems operate.

Chicago MSI

I worked in a consultant position with Dr. James McLurkin, for the Chicago Museum of Science and Industry (MSI). The MSI was creating the Robot Revolution Exhibit, and the r-one robot was featured! Participants in the exhibit could use a control station on the side of an arena hosting multiple robots to command the r-ones to move in distributed swarm behaviors, such as clustering, follow-the-leader, and flocking.

ENGI 128

I was a TA for ENGI 128, Introduction to Engineering Systems, in the Fall 2014 Semester. This course is freshman-only, fun-filled introduction to concepts in mechanical engineering, electrical engineering, and computer science. The course used the r-one robot, an inexpensive robot platform developed by the MRSL. I wrote software used by the students in their assignments as well as held weekly office hours.

Summer Swarm Camp

I 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, hopefully to inspire a deep interest to pursue it further! The camp used the r-one robot, and the Python programming language.


Compuational Rock Gardening

Computational Rock Gardening

An experiment with three.js to procedurally generate aesthetically pleasing rock gardens.

Go Look! Github