From Bricks to Brains: The Embodied Cognitive Science of LEGO Robots
Bibliographic record
Abstract
Accompanying this book is additional web support that provides pdf files of traditional, "wordless" LEGO instructions for building robots, downloadable programs for controlling the robots that we describe, and videos that demonstrate robot behaviour.This web support is available at http://www.bcp.psych.ualberta.ca/~mike/BricksToBrains/.The instructional images that are provided in this book were created by first building a CAD model of the robot using the LDRAW family of programs.This is a set of programs available as freeware from http:// www.ldraw.org/.The CAD files were then converted into the instructional images using the LPUB4 program, available as freeware from www.kclague.net/LPub4.htm.Resources are available that provide detailed instructions on how to use such software tools (Clague, Agullo, & Hassing, 2002).The NXC code for the various robots described in this book was created, and downloaded to the robot, using the BricxCC utility, available as freeware at http://bricxcc.sourceforge.net/.This utility provides an excellent help file to describe the components and the syntax of the NXC programming language.All of the photographs used in Chapter 1 were taken by Nancy Digdon, and are used with her permission.The beaver in Section 1.2 was in Astotin Lake at Elk Island National Park near Edmonton, Alberta.The beaver dam in the same section is located at the nature trail in Miramichi, New Brunswick.The two images of the bald-faced hornet nest were also taken at Miramichi.The wasp nest under construction in Section 1.10 was found at Elk Lake Park, near Victoria, British Columbia.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".