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Member:sungbeanJo_paper [2021/03/04 22:00]
sungbean 		Member:sungbeanJo_paper [2021/04/21 22:08] (current)
sungbean
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-Abstract—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. +
-Imitation learning is receiving renewed interest as a +
-promising approach to training autonomous driving systems. +
-Demonstrations of human driving are easy to collect +
-at scale. Given such demonstrations,​ imitation learning can +
-be used to train a model that maps perceptual inputs to +
-control commands; for example, mapping camera images to +
-steering and acceleration. This approach has been applied to +
-lane following [27], [4] and off-road obstacle avoidance +
-However, these systems have characteristic limitations. For +
-example, the network trained by Bojarski et al. [4] was given +
-control over lane and road following only. When a lane +
-change or a turn from one road to another were required, +
-the human driver had to take control +
- +
-Why has imitation learning not scaled up to fully autonomous +
-urban driving? One limitation is in the assumption +
-that the optimal action can be inferred from the perceptual +
-input alone. This assumption often does not hold in practice: +
-for instance, when a car approaches an intersection,​ the +
-camera input is not sufficient to predict whether the car +
-should turn left, right, or go straight. Mathematically,​ the +
-mapping from the image to the control command is no longer +
-a function. Fitting a function approximator is thus bound to +
-run into difficulties. This had already been observed in the +
-work of Pomerleau: “Currently upon reaching a fork, the +
-network may output two widely discrepant travel directions,​ +
-one for each choice. The result is often an oscillation in +
-the dictated travel direction” [27]. Even if the network can +
-resolve the ambiguity in favor of some course of action, it +
-may not be the one desired by the passenger, who lacks a +
-communication channel for controlling the network itself. +
- +
-In this paper, we address this challenge with conditional +
-imitation learning. At training time, the model is given +
-not only the perceptual input and the control signal, but +
-also a representation of the expert’s intention. At test time, +
-the network can be given corresponding commands, which +
-resolve the ambiguity in the perceptuomotor mapping and +
-allow the trained model to be controlled by a passenger +
-or a topological planner, just as mapping applications and +
-passengers provide turn-by-turn directions to human drivers. +
-The trained network is thus freed from the task of planning +
-and can devote its representational capacity to driving. This +
-enables scaling imitation learning to vision-based driving in +
-complex urban environments. +
- +
-We evaluate the presented approach in realistic simulations +
-of urban driving and on a 1/5 scale robotic truck. Both +
-systems are shown in Figure 1. Simulation allows us to +
-thoroughly analyze the importance of different modeling +
-decisions, carefully compare the approach to relevant baselines,​ +
-and conduct detailed ablation studies. Experiments +
-with the physical system demonstrate that the approach can +
-be successfully deployed in the physical world. Recordings +
-of both systems are provided in the supplementary video. +
- +
-We begin by describing the standard imitation learning +
-setup and then proceed to our command-conditional formulation. +
-Consider a controller that interacts with the environment +
-over discrete time steps. At each time step t, the controller +
-receives an observation ot and takes an action at. The basic +
-idea behind imitation learning is to train a controller that +
-mimics an expert. The training data is a set of observationaction +
-pairs D = fhoi; aiigNi +
-=1 generated by the expert. The +
-assumption is that the expert is successful at performing the +
-task of interest and that a controller trained to mimic the +
-expert will also perform the task well. This is a supervised +
-learning problem, in which the parameters  of a function+
  

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