Robotic Introspection: Self-modeling
Higher animals use some form of an "internal model" of themselves for planning complex actions and predicting their consequence, but it is not clear if and how these self-models are acquired or what form they take. Analogously, most practical robotic systems use internal mathematical models, but these are laboriously constructed by engineers. While simple yet robust behaviors can be achieved without a model at all, here we show how low-level sensation and actuation synergies can give rise to an internal predictive self-model, which in turn can be used to develop new behaviors. We demonstrate, both computationally and experimentally, how a legged robot automatically synthesizes a predictive model of its own topology (where and how its body parts are connected) through limited yet self-directed interaction with its environment, and then uses this model to synthesize successful new locomotive behavior before and after damage. The legged robot learned how to move forward based on only 16 brief self-directed interactions with its environment. These interactions were unrelated to the task of locomotion, driven only by the objective of disambiguating competing internal models. These findings may help develop more robust robotics, as well as shed light on the relation between curiosity and cognition in animals and humans: Creating models through exploration, and using them to create new behaviors through introspection. Watch a movie here.
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- Other projects in this lab
Bongard J., Zykov V., Lipson H. (2006), “Resilient Machines Through Continuous Self-Modeling", Science Vol. 314. no. 5802, pp. 1118 - 1121
Adami C., (2006) "What Do Robots Dream Of?", Science Vol. 314. no. 5802, pp. 1093 - 1094
Bongard J., Lipson H. (2004), “Automated Damage Diagnosis and Recovery for Remote Robotics”, IEEE International Conference on Robotics and Automation (ICRA04), pp. 3545-3550
Bongard J., Zykov V., Lipson H. (2006) “Automated Synthesis of Body Schema using Multiple Sensor Modalities”, Proceedings of the 10th Int. Conference on Artificial Life (ALIFE X), pp.220-226.
Lipson, H., Bongard, J., Zykov, V., Malone, E., (2006) “Evolutionary Robotics for Legged Machines: From Simulation to Physical Reality”, Proceedings of the 9th Int. Conference on Intelligent Autonomous Systems, University of Tokyo, Tokyo, Japan, March 7-9, 2006, pp. 11-18.
Bongard J., Lipson H. (2005) “Automatic Synthesis of Multiple Internal Models Through Active Exploration”, AAAI Fall Symposium: From Reactive to Anticipatory Cognitive Embodied Systems, November 2005.
Applications of this concept to other domains
Aquino W., Kouchmeshky B., Bongard J., Lipson H., (2006) "Co-evolutionary algorithm for structural damage identification using minimal physical testing", Int. Journal for Numerical Methods in Engineering (in press).
Bongard J., Lipson H. (2005) “Active Coevolutionary Learning of Deterministic Finite Automata”, Journal of Machine Learning Research, 6(Oct): 1651-1678.
This project was funded by the NASA Program on Intelligent Systems and by the National Science Foundation program in Engineering Design.