Minor edit in paper

paper/paper.md

@@ -40,7 +40,7 @@ Robot simulation plays a pivotal role in robotics and machine learning research,
 
 ![Talos Humanoid robot [@stasse2017talos] in RobotDART.\label{fig:talos}](talos.png){ width=90% }
 
-# Motivation
+# Statement of need
 Simulators play a crucial role in robotics by providing a safe environment for testing without the risk of damaging real robots. Within the robotics community, existing simulators primarily prioritize seamless deployment on physical robots, which is evident in their design philosophy. One of the best examples is [Gazebo](https://gazebosim.org) [@koenig2004design], which is typically accessed through ROS, as an abstraction level, so that users can seamlessly transition between simulated and real robots. Because of this design choice, Gazebo: (1) is mostly asynchronous, which means that the simulator operates on its own clock and time-step analogous to real-world robots, (2) introduces a significant overhead, including network communication and serialization to topics/services/messages, and (3) is not designed for running multiple simulations concurrently or even sequentially. At a lower abstraction level, the robotics community has developed various physics libraries such as Mujoco [@todorov2012mujoco], Bullet [@bullet], and ODE [@smith2005open]. However, these libraries typically lack sensor abstractions, particularly for cameras, and do not come with pre-validated robot models, necessitating users to program their own simulators.
 
 With the emergence of reinforcement learning for robot control and more generally robot learning [@ravichandar2020recent] [@chatzilygeroudis2019survey] [@kroemer2021review], there arises a need for simulators that can address novel requirements not adequately addressed by current simulators or "low-level" physics libraries. Specifically, simulators designed for robot learning must encompass the following features: