Compare Gemini Robotics vs. Molmo

Gemini Robotics integrates Gemini's advanced multimodal reasoning and comprehension of the world into tangible applications, empowering robots of various forms and sizes to undertake a diverse array of real-world activities. Leveraging the capabilities of Gemini 2.0, it enhances sophisticated vision-language-action models by enabling reasoning about physical environments, adapting to unfamiliar scenarios, including novel objects, various instructions, and different settings, while also comprehending and reacting to everyday conversational requests. Furthermore, it exhibits the ability to adjust to abrupt changes in commands or surroundings without requiring additional input. The dexterity module is designed to tackle intricate tasks that demand fine motor skills and accurate manipulation, allowing robots to perform activities like folding origami, packing lunch boxes, and preparing salads. Additionally, it accommodates multiple embodiments, ranging from bi-arm platforms like ALOHA 2 to humanoid robots such as Apptronik’s Apollo, making it versatile across various applications. Optimized for local execution, it includes a software development kit (SDK) that facilitates smooth adaptation to new tasks and environments, ensuring that these robots can evolve alongside emerging challenges. This flexibility positions Gemini Robotics as a pioneering force in the robotics industry.

Molmo represents a cutting-edge family of multimodal AI models crafted by the Allen Institute for AI (Ai2). These innovative models are specifically engineered to connect the divide between open-source and proprietary systems, ensuring they perform competitively across numerous academic benchmarks and assessments by humans. In contrast to many existing multimodal systems that depend on synthetic data sourced from proprietary frameworks, Molmo is exclusively trained on openly available data, which promotes transparency and reproducibility in AI research. A significant breakthrough in the development of Molmo is the incorporation of PixMo, a unique dataset filled with intricately detailed image captions gathered from human annotators who utilized speech-based descriptions, along with 2D pointing data that empowers the models to respond to inquiries with both natural language and non-verbal signals. This capability allows Molmo to engage with its surroundings in a more sophisticated manner, such as by pointing to specific objects within images, thereby broadening its potential applications in diverse fields, including robotics, augmented reality, and interactive user interfaces. Furthermore, the advancements made by Molmo set a new standard for future multimodal AI research and application development.