# A Motion Capture and Imitation Learning Based Approach to Robot Control
## 6 Results  
In our research, numerous models were trained on both of the data sets described in Section 5.1, in both of the architectures discussed in Section 5.3. After the initial trial and error process, a systematic training regimen produced the following:  
* Feedforward networks--a total of 48 models, varying the training data set, presence of a time signal in the features, perceptron counts per hidden layer, and training duration;
* RNNs--36 models with data set, learning rate, model size, and training length being the variable parameters. Figure 5 illustrates how model outputs were visualized to use their qualitative aspects in guiding hyperparameter choice, and a preliminary implementation on a real robot was achieved--executing trajectories generated by the simpler feedforward network, with target coordinates sampled from normal distributions corresponding to their values in the training data set. As can be seen, when these outputs were used to generate motion plans for a _Universal Robots UR5e_ robot equipped with a pneumatic gripper, a plastic bottle was successfully thrown into a target container located outside the robot's reachable volume. Adequate output sequences were attained with both model architectures at multiple hy  
perparameter combinations, so to draw specific conclusions, the quantitative evaluation metrics discussed in Section 5.5 were produced for the 84 systematically trained models described above. Of the feedforward models, 18 out of 48 (37.5%) successfully triggered a gripper release on every evaluation trajectory, enabling mean throw error estimates to be computed. Among the RNNs, this was true for 24 out of 36 (66.7%). Figure 6 shows a broad comparison between the RNN and feedforward model architectures, with each of the evaluation metric classes being present, though not all the specific metrics discussed--as there was found to be a considerable amount of duplication in their findings. All figures shown correspond to the best result attained by either type in each of the performance indicators, irrespective of other variables. Figure 7 elaborates upon each model class, showing the best attained results at various hyperparameter values. In the case of the feedforward model, the input observations did not initially contain a time signal, which was rectified early on. In the case of the RNN, deviating from the default learning rate was found to be beneficial. For both architectures, the best performance at each epoch count and parameter count is also plotted. Figure 8 shows the impact of only varying the crucial size (perceptron count)--in each case, the two best performing models at each fixed parameter combination are selected and their performance at different values of the independent variable is graphed. Figure 6: Results—comparison between model architectures (_Naive_—refers to the feedforward architecture, also “naive behavioral cloning”), the best performance attained in each class. Top row metrics—cosine similarity (dimensionless), Euclidean distance (all features, no specific unit); mean position error (stepwise, meters). Bottom row—mean rotation error (stepwise, radians), mean throw error (meters). Figure 8: Results—cosine similarity (dimensionless) and mean throw error (meters) metrics for feedforward and recurrent models, only varying in the perceptron count parameter (others held constant)—best 2 on train and validation sets each. Figure 7: Results—best mean throw error performance for each hyperparameter value, in units of meters. The top row shows feedforward model results, the bottom—RNN. In both cases, the smaller, newer dataset with less variance in orientation and target coordinates results in better throw evaluations. Introducing the time signal significantly improves feedforward model performance, whereas for the RNN, improved results could be attained at higher learning rates.