SA-Net - Robust State-Action Recognition for Learning from Observations

Nihal Soans
Nihal Soans
Ehsan Asali
Ehsan Asali
Yi Hong
Yi Hong

ICRA, pp. 2153-2159, 2020.

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Abstract:

Learning from observation (LfO) offers a new paradigm for transferring task behavior to robots. LfO requires the robot to observe the task being performed and decompose the sensed streaming data into sequences of state-action pairs, which are then input to LfO methods. Thus, recognizing the state-action pairs correctly and quickly in sens...More

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