The development of general-purpose robots capable of performing a wide range of tasks independently is gaining momentum, driven by recent advances in artificial intelligence. These robots, which could one day assist in workplaces or homes, are being designed to operate in complex and unpredictable environments without direct human supervision. According to Matt Malchano, vice president of software at Boston Dynamics, the concept of robot autonomy has evolved significantly over the years, expanding from basic navigation to handling a vast array of tasks.
Modern AI techniques, such as reinforcement learning and large foundation models, are playing a crucial role in advancing robot autonomy. These methods allow robots to learn from trial-and-error and leverage pre-trained data to understand and adapt to new situations. Sergey Levine, a computer scientist at the University of California Berkeley, explained that the combination of these AI techniques has enabled significant progress in training robots to perform various tasks reliably under different conditions. He emphasized that achieving practical robotic intelligence requires handling complex environmental perception, robust motor skills, and the ability to generalize behaviors to new situations.
The International Standards Organization defines autonomy in robotics as the ability to perform intended tasks based on current state and sensing without human intervention. Researchers and companies are now focusing on developing general-purpose AI models that can power different types of robots suited for specific jobs, such as a small robotic arm for apartments or a large robot for farms. However, challenges remain, including the need for more training data and the complexities of real-world environments.
Source: arstechnica
