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# Tejus Gupta tejus-gupta

Created Apr 19, 2019
 if message_type == 'user_read': key,_ = data.split('|') highest_version = -1 highest_version_value = '' #nodes = random.sample([(hash(key)+i)%N for i in range(R)], Q_r) #nodes = random.sample(get_next_live_inc(hash(key), R), Q_r) nodes = get_next_live_inc(hash(key), R)
Created Mar 29, 2019
Planner Files
View example.launch

Last active Apr 9, 2021
View lattice-planner.md

### State lattice-based planning

#### tl;dr

The kinodynamics constraints of the robot are encoded in the state lattice graph and any path in this graph is feasible. After constructing the graph, any graph search algorithm can be used for planning.

#### Algorithm

A robot's configuration space is usually discretized to reduce computational complexity of planning at the expense of completeness. However, it is difficult to search this space while satisfying the robot's differential constraints. State lattices are a special way of discretization of robot state space that ensures (by construction) that any path in the graph complies with the robot's constraints, thereby eliminating the need to consider them explicitly during planning.

Created Nov 28, 2018
View remove.py
 to_remove = [] for class_idx in selected_classes: if np.sum(y_train[:, class_idx] < 0.5) < 5 or np.sum(y_train[:, class_idx] > 0.5) < 5: to_remove.append(class_idx) for class_idx in to_remove: selected_classes.remove(class_idx) print('Removed classes with too few examples')
Last active Oct 30, 2018
 \begin{figure*} \begin{center} \includegraphics[width=3.132in,height=2.349in]{Figure_1-11.png} \hspace{0.2cm} \includegraphics[width=3.132in,height=2.349in]{Figure_1-6.png} \end{center} \begin{center} \caption{A. (left) Ratio of $i^{th}$ singular value to first singular value of matrix $P$ containing example-wise adversarial perturbations. B. (right) Cosine similarity of our universal perturbation for class '0' with singular vectors of matrix $P$.}