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Visual Dynamics Models for Robotic Planning and Control

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Figure 2.2: Dilated VGG-16 net- net-work. The intermediate feature maps drawn in a lighter shade are outputs of max-pooling layers
Figure 2.5: Costs of test executions using various feature dynamics models, where the feature weights are optimized with FQI
Table 2.1: Sample observations from test executions in our experiments with the novel cars, and the costs for each trajectory, for different feature dynamics
Figure 2.6: Comparison of costs on test executions of prior methods against our method based on VGG conv4 3 feature dynamics
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Sampling-Based MPC with Continuous and Discrete Actions Video Prediction with Stochastic Adversarial Models Reinforcement Learning and Modeling Experimental Evaluation Experiment Details

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