About

I'm a Ph.D. student in Computer Science at Rice University, working with Dr. Lydia Kavraki. I received my B.S. in Computer Science from Rice in Spring 2016, and my M.S. in Computer Science from Rice in December 2017. My research interest is in algorithmic robotics, focused on studying the difficulties in motion planning that arise in robotic manipulation.

"zak" rice.edu / Duncan Hall 3052

Github / Google Scholar / ResearchGate

A picture of me from Spring 2016.

Research & Experience

Kavraki Lab @ Rice University

I am a 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
I am an NSTRF Fellow with NASA Johnson Space Center's Dexterous Robotics Lab, where I've been developing the motion planning system for NASA’s humanoid robot Robonaut 2.

MRSL @ Rice University
I used to work in the Multi-Robot Systems Lab (MRSL) at Rice University, working on multi-robot manipulation.

Publications

[1]

Sampling-Based Methods for Motion Planning with Constraints
Z. Kingston, M. Moll, and L. E. Kavraki., 2018. To Appear.

BibTeX

@article{Kingston2018,
    author = {Kingston, Z. and Moll, M. and and Kavraki, L. E.},
    title = {Sampling-Based Methods for Motion Planning with Constraints},
    journal = {Annual Review of Control, Robotics, and Autonomous Systems},
    year = {2018},
    note = {(To Appear)}
}

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[2]

A Unifying Framework for Constrained Sampling-Based Planning
Z. Kingston., 2017. M.S. Thesis.

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 the menagerie of 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,
  author = {Kingston, Z.},
  title = {A Unifying Framework for Constrained Sampling-Based Planning},
  school = {Rice University},
  address = {Houston, TX}
  year = {2017},
}

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[3]

Decoupling Constraints from Sampling-Based Planners
Z. Kingston, M. Moll, and L. E. Kavraki., 2017.

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,
  author = {Kingston, Z. and Moll, M. and and Kavraki, L. E.},
  title = {Decoupling Constraints from Sampling-Based Planners},
  booktitle = {International Symposium on Robotics Research},
  year = {2017},
}

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[4]

Robonaut 2 and You: Specifying and Executing Complex Operations
W. Baker, Z. Kingston, M. Moll, J. Badger, and L. E. Kavraki., 2017.

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,
  author = {Baker, W. and Kingston, Z. and Moll, M. and Badger, J. and Kavraki, L. E.},
  title = {Robonaut 2 and You: Specifying and Executing Complex Operations},
  booktitle = {IEEE Workshop on Advanced Robotics and its Social Impacts},
  year = {2017},
}

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[5]

Incremental Task and Motion Planning: A Constraint-Based Approach
N. T. Dantam, Z. Kingston, S. Chaudhuri, and L. E. Kavraki., 2016.

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,
  author = {Dantam, N. T. and Kingston, Z. and Chaudhuri, S. and Kavraki, L. E.},
  title = {Incremental Task and Motion Planning: A Constraint-Based Approach},
  booktitle = {Robotics: Science and Systems},
  year = {2016},
}

Close

[6]

Kinematically Constrained Workspace Control via Linear Optimization
Z. Kingston, N. T. Dantam, and L. E. Kavraki., 2015.

Abstract / BibTeX / PDF / Poster / 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,
  author = {Kingston, Z. and Dantam, N. T. and Kavraki, L. E.},
  booktitle = {International Conference on Humanoid Robots},
  title = {Kinematically Constrained Workspace Control via Linear Optimization},
  year = {2015},
}

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[7]

Pipelined Consensus for Global State Estimation in Multi-Agent Systems
G. Habibi, Z. Kingston, Z. Wang, M. Schwager, and J. McLurkin., 2015.

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,
  author = {Habibi, G. and Kingston, Z. and Wang, Z. and Schwager, M. and McLurkin, J.},
  booktitle = {International Conference on Autonomous Agents and Multiagent Systems},
  title = {Pipelined Consensus for Global State Estimation in Multi-Agent Systems},
  year = {2015},
}

Close

[8]

Distributed Centroid Estimation and Motion Controllers for Collective Transport by Multi-Robot Systems
G. Habibi, Z. Kingston, W. Xie, M. Jellins, and J. McLurkin., 2015.

Abstract / BibTeX / PDF / Publisher / Video

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{Habibi2015b,
  author = {Habibi, G. and Kingston, Z. and Xie, W. and Jellins, M. and McLurkin, J.},
  booktitle={IEEE International Conference on Robotics and Automation},
  title={Distributed Centroid Estimation and Motion Controllers for Collective Transport by Multi-Robot Ssystems},
  year={2015},
}

Close


Teaching & Outreach

COMP 450/550

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

COMP 321

I am an in-lab TA for COMP 321, Introduction to Computer Systems, Spring 2015/2018. COMP 321 uses the C programming language to teach how modern computer systems operate.

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. Read about it in the Rice at Large publication.

COMP 140

I 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.

Chicago MSI

I 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 of r-ones with distributed behaviors.

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 developed by the MRSL.

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. The camp used the r-one robot, and the Python programming language.


Awards

NSTRF

I 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

I 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

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

Distinction in Research

I was awarded the Distinction in Research and Creative Works.


Miscellaenous

Coroga

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

Bassist Functions

In my spare time, I play in a band with some friends from applied mathematics. Check us out on SoundCloud!