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--- Member:sungbeanJo_paper [2021/03/04 18:14]
sungbean
+++ Member:sungbeanJo_paper [2021/04/21 22:08] (current)
sungbean
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-Deep networks trained on demonstrations of human
+get_config_param active timestamp_mode
-driving have learned to follow roads and avoid obstacles.
+	TIME_FROM_INTERNAL_OSC
-However, driving policies trained via imitation learning cannot
+get_config_param active multipurpose_io_mode
-be controlled at test time. A vehicle trained end-to-end to imitate
+	OUTPUT_OFF
-an expert cannot be guided to take a specific turn at an upcoming
+get_config_param active sync_pulse_in_polarity
-intersection. This limits the utility of such systems. We
+	ACTIVE_LOW
-propose to condition imitation learning on high-level command
+get_config_param active nmea_in_polarity
-input. At test time, the learned driving policy functions as a
+	ACTIVE_HIGH
-chauffeur that handles sensorimotor coordination but continues
+get_config_param active nmea_baud_rate
-to respond to navigational commands. We evaluate different
+	BAUD_9600
-architectures for conditional imitation learning in vision-based
-driving. We conduct experiments in realistic three-dimensional
-simulations of urban driving and on a 1/5 scale robotic truck
-that is trained to drive in a residential area. Both systems
-drive based on visual input yet remain responsive to high-level
-navigational commands.

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