Correct typos and 'proprioception'

sections/01_introduction.tex

@@ -83,7 +83,7 @@ \subsection{Code Example: Batching a (Streaming) Dataset}
 In practice, most reinforcement learning (RL) and behavioral cloning (BC) algorithms tend to operate on stack of observation and actions.
 For the sake of brevity, we will refer to joint spaces, and camera frames with the single term of \emph{frame}.
 For instance, RL algorithms may use a history of previous frames \(o_{t-H_o:t} \) to mitigate partial observability, and BC algorithms are in practice trained to regress chunks of multiple actions (\(a_{t+t+H_a} \)) rather than single controls.
-To accommodate for these specifics of robot learning training, \lerobotdataset~provides a native windowing operation, whereby users can define the \emph{seconds} of a given window (before and after) around any given frame, by using the \texttt{delta\_timestemps} functionality.
+To accommodate for these specifics of robot learning training, \lerobotdataset~provides a native windowing operation, whereby users can define the \emph{seconds} of a given window (before and after) around any given frame, by using the \texttt{delta\_timestamps} functionality.
 Unavailable frames are opportunely padded, and a padding mask is also returned to filter out the padded frames.
 Notably, this all happens within the \lerobotdataset, and is entirely transparent to higher level wrappers commonly used in training ML models such as \texttt{torch.utils.data.DataLoader}.
 

sections/02_classic_robotics.tex

@@ -12,15 +12,15 @@ \subsection{Explicit and Implicit Models}
 \begin{figure}
     \centering
     \includegraphics[width=0.5\linewidth]{figures/ch2/ch2-approaches.pdf}
-    \caption{Overview of methods to generate motion (clearly non-exhausitve, see~\citet{bekrisStateRobotMotion2024}). The different methods can be grouped based on whether they explicitly (\emph{dynamics-based}) or implicitly (\emph{learning-based}) model robot-environment interactions.}
+    \caption{Overview of methods to generate motion (clearly non-exhaustive, see~\citet{bekrisStateRobotMotion2024}). The different methods can be grouped based on whether they explicitly (\emph{dynamics-based}) or implicitly (\emph{learning-based}) model robot-environment interactions.}
     \label{fig:generating-motion-atlas}
 \end{figure}
 
 Robotics is concerned with producing artificial motion in the physical world in useful, reliable and safe fashion.
-Thus, robotics is an inherently multi-disciplinar domain: producing autonomous motion in the physical world requires, to the very least, interfacing different software (motion planners) and hardware (motion executioners) components.
+Thus, robotics is an inherently multidisciplinary domain: producing autonomous motion in the physical world requires, to the very least, interfacing different software (motion planners) and hardware (motion executioners) components.
 Further, knowledge of mechanical, electrical, and software engineering, as well as rigid-body mechanics and control theory have therefore proven quintessential in robotics since the field first developed in the 1950s.
 More recently, Machine Learning (ML) has also proved effective in robotics, complementing these more traditional disciplines~\citep{connellRobotLearning1993}.
-As a direct consequence of its multi-disciplinar nature, robotics has developed as a rather wide array of methods, all concerned with the main purpose of \highlight{producing artificial motion in the physical world}.
+As a direct consequence of its multidisciplinary nature, robotics has developed as a rather wide array of methods, all concerned with the main purpose of \highlight{producing artificial motion in the physical world}.
 
 Methods to produce robotics motion range from traditional \emph{explicit} models---\highlight{dynamics-based}\footnote{In here, we refer to both \emph{kinematics} and \emph{dynamics}-based control.} methods, leveraging precise descriptions of the mechanics of robots' rigid bodies and their interactions with eventual obstacles in the environment---to \emph{implicit} models---\highlight{learning-based} methods, treating artificial motion as a statistical pattern to learn given multiple sensorimotor readings~\citep{agrawalComputationalSensorimotorLearning,bekrisStateRobotMotion2024}.
 A variety of methods have been developed between these two extrema.