Software Learns From Errors
http://ccsl.mae.cornell.edu/
More specifically:
http://ccsl.mae.cornell.edu/emergent_self_models
Resilient Robotics and Active Fault-Tolerant Systems
Project members: Josh Bongard, Victor Zykov, and Hod Lipson (see team picture).
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.
Next
Full Page
http://ccsl.mae.cornell.edu/
More specifically:
http://ccsl.mae.cornell.edu/emergent_self_models
Resilient Robotics and Active Fault-Tolerant Systems
Project members: Josh Bongard, Victor Zykov, and Hod Lipson (see team picture).
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.
Next
Full Page
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