The Rapid Evolution Of Robotics
A few days ago, I attended one of the first non-academic robotics conferences, Actuate , in San Francisco. One of the most fascinating insights was the early evidence suggesting that robots trained using a variety of data ( cross-embodiment training ) can outperform specialized robots at their own tasks. For example, a robot trained on diverse tasks—such as walking, assembling, and manufacturing—could perform better at picking objects than a robot trained exclusively for that task. Robots in our life (image generated by DALL-E) Following are my key observations from the conference: 1. Physical Intelligence , a silicon valley startup, raised $70M in seed round to build a general robotics model which can be used by different kinds of robots to do various tasks. 2. This idea of general model for robotics is called Robotics Foundation Model (RFM). The concept is pretty similar to Large Language Model (LLM) which are the basis for Generative Artificial Intelligence (AGI). 3. There is