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docs/src/assets/diagram.drawio

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docs/src/index.md

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 # TORA.jl
 
-Documentation for TORA.jl
+*TORA* stands for **T**rajectory **O**ptimization for **R**obot **A**rms.
+
+To get started, see the [Tutorial](@ref).
+
+## Summary
+
+This package allows users to define tasks for robot manipulators with simple *high-level descriptions*. Then, TORA.jl does the heavy-lifting!
+It converts those descriptions into numerical optimization problems, which are in turn tackled by state-of-the-art solvers.
+The final result of the optimization is a full trajectory (joint *positions*, joint *velocities*, and joint *torques*) taking into account the whole-body dynamics of the system.
+These trajectories can be commanded to your favourite robot, either in simulation or in real life.
+
+```@raw html
+<figure>
+    <img src="./assets/diagram.svg" alt="diagram" width="100%">
+    <figcaption><strong>Diagram 1.</strong> Intended use of this package.</figcaption>
+</figure>
+```
+
+## Functionality
+
+Currently, the highlights of TORA.jl are as follows:
+
+- Simple interface to define constrained motion-planning problems
+- Formulation of the optimal control problem using *Direct Transcription*
+- Optimization of NLP problems using state-of-the-art solvers ([Ipopt.jl](https://github.com/jump-dev/Ipopt.jl) and [KNITRO.jl](https://github.com/jump-dev/KNITRO.jl))
+- Full system dynamics enforced with either *forward* or *inverse* dynamics ([RigidBodyDynamics.jl](https://github.com/JuliaRobotics/RigidBodyDynamics.jl))
+- Automatic differentiation of sparse Jacobians ([ForwardDiff.jl](https://github.com/JuliaDiff/ForwardDiff.jl) and [SparseDiffTools.jl](https://github.com/JuliaDiff/SparseDiffTools.jl))
+- Automatic sparsity detection of Jacobians ([SparsityDetection.jl](https://github.com/SciML/SparsityDetection.jl))
+- Visualization of robot models and motion plans ([MeshCat.jl](https://github.com/rdeits/MeshCat.jl))
+
+## About
+
+Overall, the *direct transcription* technique implemented in TORA.jl stems from:
+- Betts, John T. [*Practical Methods for Optimal Control and Estimation Using Nonlinear Programming*](https://epubs.siam.org/doi/book/10.1137/1.9780898718577). SIAM, 2010.

docs/src/tutorial.md

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 # Tutorial
 
+This tutorial assumes you have already installed Julia and TORA.jl. See [Installation](@ref).
+
 !!! info
     A [Jupyter](https://jupyter.org/) notebook of this tutorial is available [here](https://github.com/ferrolho/TORA.jl/blob/main/notebooks/Tutorial.ipynb).