Supervising Robot Learning with Web-Sourced Language and Video Data
Researchers from Stanford University's SAIL and CRFM propose a novel approach to overcome the challenges of training versatile home robots capable of generalizing to new environments. While deep learning has advanced robotic grasping and locomotion, creating robots that adapt to diverse tasks without extensive retraining remains difficult. Traditional methods like imitation learning require expensive expert data, while offline reinforcement learning struggles with defining scalable, hard-coded reward functions. To address this, the team explores using easily accessible web data for scalable supervision. They demonstrate how crowdsourced natural language descriptions of robot videos can effectively learn rewards for multiple tasks within a single environment, simplifying the annotation process compared to scalar rewards. Furthermore, by combining robot data with diverse 'in-the-wild' human videos from platforms like YouTube, the learned reward functions can generalize in a zero-shot manner to unseen tasks and environments. This method leverages the success of foundation models in NLP and vision, applying similar principles of massive, diverse datasets to robotics. The approach aims to enable robots to perform interactive tasks, such as cooking and cleaning, in unstructured real-world settings by utilizing scalable, low-cost supervision derived from internet resources.
Wire timeline
Supervising Robot Learning with Web-Sourced Language and Video Data
Researchers from Stanford University's SAIL and CRFM propose a novel approach to overcome the challenges of training versatile home robots capable of generalizing to new environments. While deep learning has advanced robotic grasping and locomotion, creating robots that adapt to diverse tasks without extensive retraining remains difficult. Traditional methods like imitation learning require expensive expert data, while offline reinforcement learning struggles with defining scalable, hard-coded reward functions. To address this, the team explores using easily accessible web data for scalable supervision. They demonstrate how crowdsourced natural language descriptions of robot videos can effectively learn rewards for multiple tasks within a single environment, simplifying the annotation process compared to scalar rewards. Furthermore, by combining robot data with diverse 'in-the-wild' human videos from platforms like YouTube, the learned reward functions can generalize in a zero-shot manner to unseen tasks and environments. This method leverages the success of foundation models in NLP and vision, applying similar principles of massive, diverse datasets to robotics. The approach aims to enable robots to perform interactive tasks, such as cooking and cleaning, in unstructured real-world settings by utilizing scalable, low-cost supervision derived from internet resources.
The Stanford AI Lab Blog