Physics Sim2Real Gap (continued) System identification errors Controller in the loop of policy control A robot typically consists of multiple actuators such as gear motors, each tasked with moving one or more joints.

Why Scaling Hasn’t Happened in the World of Robotics Navigating the landscape of general-purpose robots today, one of the most pressing challenges we face revolves around the lack of data. This scarcity of data, particularly in terms of robot action, stands as a major roadblock hindering the widespread adoption of deep learning models within real-world robotic applications.

大语言模型的输出可以分成两类：第一种是形而上无法被现阶段科学

先说答案，学到了但也没学到。 这篇文章的标题里有两个概念，一个

I’ve been asked by quite a few people about what “language grounding” means. So I think I’ll write a short post explaining it, and specifically arguing why it is important to a truly intelligent agent.

Recently, I was working on a project that requires learning a latent representation with disentangled factors for high-dimensional inputs. For a brief introduction to disentanglement, while we could use an autoencoder (AE) to compress a high-dimensional input into a compact embedding, there is always dependence among the embedding dimensions, meaning that multiple dimensions always change together in a dependent way.

The other day I was reading the article “You and your research”, transcribed from a seminar by Richard Hamming. There is one paragraph about “choosing important problems” which I think is inspirational:

Assume that we have a policy $\pi(a_{0:k-1}|s_0)$ that is able to output a variable length $k$ of actions given any state $s_0$. We define a Q function $Q(s_0,a_{0:k-1})$ which computes the state-action value of any action sequence $a_0,a_1,\ldots,a_{k-1}$ given $s_0$.