UBC Theses and Dissertations
Embodied perception during walking using Deep Recurrent Neural Networks Chen, Jacob
Movements such as walking require knowledge of the environment in order to be robust. This knowledge can be gleaned via embodied perception. While information about the upcoming terrain such as compliance, friction, or slope may be difficult to directly estimate, using the walking motion itself allows for these properties to be implicitly observed over time from the stream of movement data. However, the relationship between a parameter such as ground compliance and the movement data may be complex and difficult to discover. In this thesis, we demonstrate the use of a Deep LSTM Network to estimate slope and ground compliance of terrain by observing a stream of sensory information that includes the character state and foot pressure information.
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