The role(s) will generative AI play in the future of robotics? The current rate of change makes it hard to predict very far into the future. Foundation models represent a major shift in how the best machine learning models are created, and we are already seeing some impressive near-term accelerations in natural language interfaces. They offer opportunities to create conversational interfaces to our robots, improve the quality of existing computer vision functions and potentially enable new customer-facing capabilities such as visual question answering. Ultimately we feel these more scalable architectures and training strategies are likely to extend past language and vision into robotic planning and control. Being able to interpret the world around a robot will lead to a much richer understanding on how to interact with it. It’s a really exciting time to be a roboticist! Thoughts on the humanoid form factor? Humanoids aren’t necessarily the best form factor for all tasks. Take Stretch, for example — we originally generated interest in a box-moving robot from a video we shared of Atlas moving boxes. Just because humans can move boxes doesn’t mean we’re the best form factor to complete that task, and we ultimately designed a custom robot in Stretch that can move boxes more efficiently and effectively than a human. With that said, we see great potential in the long-term pursuit of general-purpose robotics, and the humanoid form factor is the most obvious match to a world built around our form. We have always been excited about the potential of humanoids and are working hard to close the technology gap.
Full interview : Boston Dynamics’ Aaron Saunders opines on the future of robotics, the role of artificial generative intelligence in robotics, humanoids, and much more.
