Robot Representation

Robot representation research focuses on creating effective ways for robots to understand and interact with their environments, encompassing both physical manipulation and social interaction. Current efforts center on developing robust and generalizable representations using techniques like transformer networks, graph neural networks, and diffusion models, often leveraging large-scale pre-training on video data to improve performance on downstream tasks. These advancements are crucial for enabling robots to perform complex tasks in diverse and unpredictable settings, bridging the gap between low-level sensor data and high-level semantic understanding, and ultimately leading to more capable and adaptable robotic systems.