A Bottom-Up Approach to Intelligence

This talk will showcase unprecedented natural behaviors in performing dynamic tasks on the Boston Dynamics Atlas humanoid robot, marking a major advance in closing the gap between human characters in graphics and physical humanoids in robotics.In this work, we employed a bottom-up approach to facilitate physical intelligence.Our primary focus was on streamlining the zero-shot sim-to-real transfer of policies that were trained to mimic stylized kinematic motions, either captured from humans or designed by animators.By carefully selecting components of our Reinforcement Learning (RL) framework and automating the deployment process on the hardware, we achieved high-quality motions while minimizing excessive domain randomization and avoiding the need for complicated reward shaping.While this work focuses on embodied physical intelligence, the progress in the broader field of Artificial Intelligence (AI) has largely been driven by advances in perception and cognitive intelligence.The human cognitive imprint on the Internet has fueled the rise of Foundation Models, such as LLMs and VLMs, bringing us closer than ever to the holy grail of AI: creating an agent that understands the world and acts accordingly.Yet, despite these remarkable advances in the virtual realm, the "act" component of AI continues to lag behind.Embodied intelligence has proven challenging, as the physical agent must grapple with the complexity, uncertainty, and variability of the real world.While it is tempting to apply the same methodologies that revolutionized virtual domains, scaling up models and datasets, a crucial difference remains: unlike the rich, structured, and abundant data on the Internet, human motion data is sparse, incomplete, and typically lacks explicit action labels.To address this challenge, some researchers have turned to teleoperation to collect in-morphology motion data with action labels,