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Gregory S. Chirikjian

Gregory S. Chirikjian is a astronomy topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Gregory S. Chirikjian rather than just read about it. In short: Gregory Scott Chirikjian (born 1966) is an American roboticist and applied mathematician, primarily working in the field of kinematics, motion planning, computer vision, group theory applications in engineering, and the mechanics of macromolecules. A longtime professor of Mechanical Engineering at Johns Hopkins University, he recently served as the chair and professor at the Department of Mechanical Engineering at t…

Gregory S. Chirikjian — main illustration
Gregory S. Chirikjian — illustration

Key takeaways

  • Gregory S. Chirikjian belongs to astronomy; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Gregory S. Chirikjian to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Gregory S. Chirikjian from memory before moving on to harder problems.

Reference excerpt

Gregory Scott Chirikjian (born 1966) is an American roboticist and applied mathematician, primarily working in the field of kinematics, motion planning, computer vision, group theory applications in engineering, and the mechanics of macromolecules. A longtime professor of Mechanical Engineering at Johns Hopkins University, he recently served as the chair and professor at the Department of Mechanical Engineering at the University of Delaware, and is now a professor in the Robotics Department at Mohamed bin Zayed University of Artificial Intelligence in Abu Dhabi. He is known for his theoretical contributions to the kinematics of hyper-redundant (snake-like and continuum) robots and stochastic methods on Lie groups.

Academic life Chirikjian received a bachelor's degree from Johns Hopkins University (JHU), Baltimore, MD, USA, in 1988, and the Ph.D. degree from the California Institute of Technology, Pasadena, CA, USA, in 1992. In the same year, he joined the Department of Mechanical Engineering at Johns Hopkins University as an assistant professor. He was promoted to associate professor and full professor in 1997 and 2001, respectively. From 2004 to 2007, he was the Chair of the Department of Mechanical Engineering, Johns Hopkins University. From 2014 to 2015, he served as a program director for the US National Robotics Initiative, which included responsibilities in the Robust Intelligence cluster in the Information and Intelligent Systems Division of CISE at the National Science Foundation (NSF). From 2019 to 2023, he was the Chair of the Department of Mechanical Engineering, National University of Singapore. Unitl recently he was the chair of the Department of Mechanical Engineering at University of Delaware. and is now a professor in the Robotics Department at Mohamed bin Zayed University of Artificial Intelligence in Abu Dhabi.

Selected Research Accomplishments Chirikjian's PhD work established the concept of `hyper-redundant' (snakelike and continuum) robots and developed framed `backbone curve' models to represent such robots both in terms of their geometrical and inertial properties. These models essentially form the basis for all studies in continuum robots today. In the 1990s he pioneered `metamorphic' (modular self-reconfigurable) robotic systems, in an attempt to make liquid-like robots that can morph into various shapes, as in a well-known SciFy movie from 1991 that inspired his work on this topic. In the 2000s Chirikjian worked on robotic self-replication and self-repair. He also applied earlier work on hyper-redundant robotic manipulators to model DNA statistical mechanics, as well as developing computer models of how protein molecules change shape as part of their function. In the 2010s, he initiated the use of Lie-theoretic methods (e.g., the closed-form `banana distribution') to model uncertainty propagation in robotics, which has become the cornerstone of invariant Kalman filtering. He observed that essentially the same mathematical model that describes DNA statistical mechanics can be used to model the uncertainty in a vehicle's position and orientation, by replacing the framed DNA backbone curve with the time-parameterized trajectory traversed by the vehicle. Starting at this time he also began work on closed-form mathematical expressions for the boundaries of Minkowski sums of ellipsoids, and then generalized that result to Minkowski sums of any bodies with smooth positively curved boundaries. In the 2020s, he has moved into the area of physical AI, affordance-based reasoning, and `Robot Imagination' while continuing to make contributions to earlier topics. During the COVID lockdown in Singapore, he and his team made rapid progress on this, as well as on completing the Minkowski sum work. Recently, his research group has integrated this into robot motion planning, collision avoidance, and computer vision algorithms that support the concept of `Robot Imagination'.

Awards and honors Chirikjian was named NSF's Young Investigator in 1993, Presidential Faculty Fellow in 1994, and was a recipient of the ASME Pi Tau Sigma Gold Medal in 1996. He was elected as a fellow of ASME in 2008, and a fellow of IEEE in 2010 for his contributions to hyper-redundant manipulators. In 2019, he received the American Society of Mechanical Engineers' Machine Design Award.

References

External links Gregory Scott Chirikjian at GitHub

Illustrations

Gregory S. Chirikjian illustration

Worked examples

Example 1 — a first encounter with Gregory S. Chirikjian

Start with the simplest possible case. Write down what Gregory S. Chirikjian claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In astronomy, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Gregory S. Chirikjian before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Gregory S. Chirikjian ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Gregory S. Chirikjian

In research
Gregory S. Chirikjian appears in astronomy research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Gregory S. Chirikjian in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Gregory S. Chirikjian is common in secondary-school and first-year university syllabi. It links to neighbouring topics 1966 births, 20th-century American mathematicians, Academic staff of the National University of Singapore, so understanding it makes those chapters shorter.
In everyday life
Look for Gregory S. Chirikjian outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.
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How to study Gregory S. Chirikjian in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Gregory S. Chirikjian means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Gregory S. Chirikjian out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Gregory S. Chirikjian in simple terms?

Gregory Scott Chirikjian (born 1966) is an American roboticist and applied mathematician, primarily working in the field of kinematics, motion planning, computer vision, group theory applications in engineering, and the mechanics of macromolecules. A longtime professor of Mechanical Engineering at…

Why does Gregory S. Chirikjian matter?

Because it connects several astronomy ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Gregory S. Chirikjian?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Gregory S. Chirikjian.

Tags

  • 1966 births
  • 20th-century American mathematicians
  • Academic staff of the National University of Singapore
  • American applied mathematicians
  • American expatriate academics
  • American expatriates in Singapore
  • American roboticists
  • Fellows of the American Society of Mechanical Engineers
  • Fellows of the IEEE
  • Johns Hopkins University alumni
  • Johns Hopkins University faculty
  • Living people

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