请问HN：机器人技术能力的研究是否会加速人工通用智能（AGI）的时间表？
为了提供背景信息，我是一名即将毕业的数学与计算机科学本科生，考虑在理论机器人领域发展职业，特别是在持续学习和开发能够像人类一样学习、适应和导航其环境的机器人方面。然而，我有一个担忧，那就是这样的研究可能会加速通用人工智能（AGI）的进程。具体来说，似乎有可能为机器人开发的持续学习架构可以转移到通用AGI系统上（即使这些AGI系统没有实体，因为持续适应和长期目标追求等能力可能超越物理任务而具有普遍性）。
这是一个合理的担忧吗？在人工智能安全社区中，这种看法是否普遍？也就是说，主流的人工智能安全研究人员是否会认为这两个方向在某种程度上对AGI能力有实质性的贡献？或者是否有充分的理由相信，机器人领域的持续学习研究不会显著加速AGI的进程？非常感谢您提供诚实的看法。
简而言之：机器人能力研究是否很可能实质性地加速AGI的进程？如果是，为什么？
查看原文
For context, I am a final-year math + CS undergraduate considering pursuing a career in theoretical robotics, particularly in continual learning and the development of robots that can learn from and adapt to / navigate their environments in a human-like manner. One concern I have, however, is that such research might advance AGI timelines. Specficially, it seems possible that architectures developed for continual learning in robots could transfer to general AGI systems (even if the AGI systems are non-embodied, since capabilities such as continual adaptation and long-term objective pursuit may generalize beyond physical tasks.)
Is this a valid concern, and is it a common view within the AI safety community? I.e. would mainstream AI safety researchers view either of these directions as meaningfully contributing to AGI capabilities? Or are there strong reasons to believe that work on continual learning in robotics would not significantly accelerate AGI timelines? Would appreciate honest perspectives.<p>TLDR: Is it very likely that robotics capabilities research meaningfully accelerates AGI timelines? If so, why?