SA-Net: Robust State-Action Recognition for Learning from Observations
Nihal Soans,Ehsan Asali,Yi Hong,Prashant Doshi,Nihal Soans,Ehsan Asali,Yi Hong,Prashant Doshi
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 sensed data is a crucial prerequisite. We present SA-Net a deep ...
