ExoStart: Efficient learning for dexterous manipulation with sensorized exoskeleton demonstrations

Recent advancements in teleoperation systems have enabled high-quality data collection for robotic manipulators, showing impressive results in learning manipulation at scale. This progress suggests that extending these capabilities to robotic hands could unlock an even broader range of manipulation skills, especially if we could achieve the same level of dexterity that human hands exhibit. However, teleoperating robotic hands is far from a solved problem, as it presents a significant challenge due to the high degrees of freedom of robotic hands and the complex dynamics occurring during contact-rich settings. In this work, we present ExoStart, a general and scalable learning framework that leverages human dexterity to improve robotic hand control. In particular, we obtain high-quality data by collecting direct demonstrations without a robot in the loop using a sensorized low-cost wearable exoskeleton, capturing the rich behaviors that humans can demonstrate with their own hands. We also propose a simulation-based dynamics filter that generates dynamically feasible trajectories from the collected demonstrations and use the generated trajectories to bootstrap an auto-curriculum reinforcement learning method that relies only on simple sparse rewards. The ExoStart pipeline is generalizable and yields robust policies that transfer zero-shot to the real robot. Our results demonstrate that ExoStart can generate dexterous real-world hand skills, achieving a success rate above 50% on a wide range of complex tasks such as opening an AirPods case or inserting and turning a key in a lock. More details and videos can be found in https://sites.google.com/view/exostart.

Gemini Robotics: Bringing AI into the Physical World

Recent advancements in large multimodal models have led to the emergence of remarkable generalist capabilities in digital domains, yet their translation to physical agents such as robots remains a significant challenge. This report introduces a new family of AI models purposefully designed for robotics and built upon the foundation of Gemini 2.0. We present Gemini Robotics, an advanced Vision-Language-Action (VLA) generalist model capable of directly controlling robots. Gemini Robotics executes smooth and reactive movements to tackle a wide range of complex manipulation tasks while also being robust to variations in object types and positions, handling unseen environments as well as following diverse, open vocabulary instructions. We show that with additional fine-tuning, Gemini Robotics can be specialized to new capabilities including solving long-horizon, highly dexterous tasks, learning new short-horizon tasks from as few as 100 demonstrations and adapting to completely novel robot embodiments. This is made possible because Gemini Robotics builds on top of the Gemini Robotics-ER model, the second model we introduce in this work. Gemini Robotics-ER (Embodied Reasoning) extends Gemini's multimodal reasoning capabilities into the physical world, with enhanced spatial and temporal understanding. This enables capabilities relevant to robotics including object detection, pointing, trajectory and grasp prediction, as well as multi-view correspondence and 3D bounding box predictions. We show how this novel combination can support a variety of robotics applications. We also discuss and address important safety considerations related to this new class of robotics foundation models. The Gemini Robotics family marks a substantial step towards developing general-purpose robots that realizes AI's potential in the physical world.