Further reading

Intelligent Systems for Robotics · optional

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None of this is taught and none of it is examined. It is here because the lectures name ideas that have a literature behind them, and some of you will want to follow them further than a slide goes.

Cognitive architectures

Week 1 derives a three-layer architecture from the fetch task and then names it. The wider question — what a complete architecture for an intelligent agent should contain — has its own field, and this course deliberately does not enter it.

• Kotseruba, I., & Tsotsos, J. K. (2020). 40 years of cognitive architectures: core cognitive abilities and practical applications. Artificial Intelligence Review, 53(1), 17–94. doi:10.1007/s10462-018-9646-y — the survey to start with: eighty-four architectures, compared on what they actually do rather than on what they claim.
• Langley, P., Laird, J. E., & Rogers, S. (2009). Cognitive architectures: Research issues and challenges. Cognitive Systems Research, 10(2), 141–160. doi:10.1016/j.cogsys.2006.07.004 — the open problems, from people who built two of the architectures.
• Newell, A. (1990). Unified Theories of Cognition. Harvard University Press. ISBN 0-674-92099-6 — the source of the argument that one architecture should account for all of cognition. Long, and worth it only if the question already interests you.
• Vernon, D. (2014). Artificial Cognitive Systems: A Primer. MIT Press. ISBN 978-0-262-02838-7 — the textbook route in, if the survey and the primary literature are too steep a first step.

Situation awareness

Week 1 overlays Endsley's three levels on the course's own layers. That mapping is a lens, not an identity: her model describes an operator's state of knowledge, not a specification for software components. The original paper is worth reading for how carefully it is scoped.

• Endsley, M. R. (1995). Toward a Theory of Situation Awareness in Dynamic Systems. Human Factors: The Journal of the Human Factors and Ergonomics Society, 37(1), 32–64. doi:10.1518/001872095779049543

Reinforcement learning

Week 2 teaches enough reinforcement learning to train the skills this course needs, and the week 2 notes page carries the reading that is taught. The two entries below are not taught. One is the survey of what the field has done on real robots, and the other is the classical controller the lecture measures a learned policy against.

• Kober, J., Bagnell, J. A., & Peters, J. (2013). Reinforcement learning in robotics: A survey. The International Journal of Robotics Research, 32(11), 1238–1274. doi:10.1177/0278364913495721 — what the field had actually got working on physical robots, and where the difficulties lie. Read it for the gap between a benchmark task and a robot.
• Åström, K. J., & Furuta, K. (2000). Swinging up a pendulum by energy control. Automatica, 36(2), 287–295. doi:10.1016/S0005-1098(99)00140-5 — the energy-shaping controller the week 2 lecture puts beside a trained policy. Worth reading to see how much of the pendulum's model the classical method needs.

Intelligent Systems for Robotics · course materials