The Pransky interview: Dr Martin Buehler, Executive R&D Imagineer at Walt Disney Imagineering and renowned expert in advanced robotics
Bibliographic record
Abstract
Purpose – The following article is a “Q & A interview” conducted by Joanne Pransky of Industrial Robot Journal as a method to impart the combined technological, business and personal experience of a prominent, robotic industry engineer-turned successful business leader, regarding the commercialization and challenges of bringing technological inventions to market while overseeing a company. The paper aims to discuss these issues. Design/methodology/approach – The interviewee is Dr Martin Buehler, Executive R & D Imagineer, at Walt Disney Imagineering. Dr Buehler is a global expert in robot manipulation and mobile robots and has led the innovative R & D and product development for some of the world’s top robot organizations. In this interview, Dr Buehler shares some of his personal and business experiences of his 25-year journey. Findings – Dr Buehler studied electrical engineering at the University of Karlsruhe and received the MSc and PhD degrees in electrical engineering from Yale University, and after a PostDoc at MIT’s Leglab in locomotion, he became a professor at McGill University in 1991, with tenure since 1997. His research focused on dynamic grasping, direct drive motor control and legged robots. From 2003 to 2008, Dr Buehler was Director of Robotics at Boston Dynamics, and he was Director of Research at iRobot Corporation from 2008 to 2011. He served as VP and General Manager of Hospital Robots for Vecna Technologies from 2011 to 2013 and Senior Director of R & D and Director, R & D Center Munich for Covidien from 2013-2015. Originality/value – Dr Buehler is best known in the academic world for his expertise in “intermittent dynamical” robotic tasks, such as dynamic manipulation and dynamically stable legged locomotion. His research led to multiple breakthroughs in legged robot projects like BigDog and RHex. In the corporate world, Buehler’s passion is to translate robotics technologies into successful product solutions. He does this by the implementation of key management strategies including Scrum and rapid and systematic experimental iteration. In addition to holding several patents, Dr Buehler is an Advisory Editorial Board member for the International Journal of Robotics Research and formerly served for ten years as the Associate Editor for the Journal of Field Robotics. Dr Buehler is a bestowed IEEE Fellow and was the recipient of the prestigious Robotics Industry Association’s 2012 Engelberger Award for Technology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.025 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".