Update song_24.md

_talks/song_24.md

@@ -6,6 +6,6 @@ date: 2024-02-16T12:30:00-0000
 location: Skilling Auditorium
 location-url: "https://campus-map.stanford.edu/?id=04-550&lat=37.42697371527761&lng=-122.17280664808126&zoom=18"
 title: "Robot Skill Acquisition: Policy Representation and Data Generation"
-abstract: "What do we need to take robot learning to the "next level?" Is it better algorithms, improved policy representations, or is it advancements in affordable robot hardware? While all of these factors are undoubtedly important, however, what I really wish for is something that underpins all these aspects – the right data. In particular, we need data that is scalable, reusable, and robot-complete. While "scale" often takes center stage in machine learning today; I would argue that in robotics, having data that is also both reusable and complete can be just as important. Focusing on sheer quantity and neglecting these properties make it difficult for robot learning to benefit from the same scaling trend that other machine learning fields have enjoyed. In this talk, we will explore potential solutions to such data challenges, shed light on some of the often-overlooked hidden costs associated with each approach, and more importantly, how to potentially bypass these obstacles."
+abstract: "What do we need to take robot learning to the 'next level?' Is it better algorithms, improved policy representations, or is it advancements in affordable robot hardware? While all of these factors are undoubtedly important, however, what I really wish for is something that underpins all these aspects – the right data. In particular, we need data that is scalable, reusable, and robot-complete. While "scale" often takes center stage in machine learning today; I would argue that in robotics, having data that is also both reusable and complete can be just as important. Focusing on sheer quantity and neglecting these properties make it difficult for robot learning to benefit from the same scaling trend that other machine learning fields have enjoyed. In this talk, we will explore potential solutions to such data challenges, shed light on some of the often-overlooked hidden costs associated with each approach, and more importantly, how to potentially bypass these obstacles."
 youtube-code: "TBD"
 ---