Vision Sim2Real Gap Two types of robot sensors When diving into the world of robotics, it’s crucial to understand the fundamental role sensors play in guiding a robot’s actions. Broadly speaking, sensors fall into two main categories: proprioceptive and exteroceptive.

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.

The output of large language models can be divided into two categories: the first is philosophical thoughts that are metaphysical and cannot be confirmed or falsified by current scientific methods,

The short answer is, they do learn, but they also fail to learn world models. The title of this article encompasses two concepts: “large language models” and “world models.” In

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 $s_0$.