How to scaling up robotics dataset?
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The seminar primarily focuses on advancing the field of robotics, particularly in making robots functional in unstructured settings outside traditional factory environments. The goal is to develop robots capable of performing a variety of tasks to address productivity challenges. The presentation acknowledges the significant progress made in robotics over the past decade but also highlights that current research is still concentrated on developing specific skills for robots.
A key point of discussion is the comparison with advancements in computer vision (CV) and natural language processing (NLP). The seminar suggests that robotics can benefit from the approaches used in these fields, such as the development of large language models and multimodal systems. It emphasizes the importance of having a good network structure, appropriate inductive bias, flexibility, expressiveness, and access to vast amounts of data.
The presentation also delves into the debate between using real-world settings versus simulators for robotics research. While the real world offers tangible scenarios, simulators provide the advantage of parallelization, flexibility, and the ability to gather a broader range of data. However, it acknowledges the existing gap between simulations and real-world applications, suggesting that if simulators are sufficiently advanced, real-world scenarios could be considered a special case within them.
In summary, the seminar by me proposes a forward-looking approach to robotics, drawing inspiration from CV and NLP, and advocates for a balance between real-world testing and simulations to accelerate the development of versatile and efficient robots.