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This paper creates a benchmark for evaluating AI systems in real-world customer delivery scenarios, where agents must clarify ambiguous requirements, work in live systems, and uncover unstated constraints. Practitioners might care because it highlights the challenges of delivering effective AI systems in complex, real-world environments.
This paper introduces GroundingPI, a new AI model that improves its ability to understand and interact with the physical world by learning to precisely identify objects and their locations, even in cluttered environments. Practitioners may care about GroundingPI's potential to enhance applications like robotics, autonomous driving, and object recognition.
This paper develops a unified model that can understand physical environments, generate actions, and predict future states, using a combination of vision, language, and embodied interactions. Practitioners may care about this model because it could be used to create robots or other agents that can interact with and adapt to their physical surroundings.
This paper introduces a new model called PDRVLA that helps robots understand the physical world from visual and language inputs, and generate actions accordingly. Practitioners in robotics and AI might care about this research because it aims to improve robots' ability to perform tasks in real-world environments.
This paper introduces Zetta, a system that enables embodied agents to learn and adapt in real-time while executing physical tasks, allowing for more efficient and effective learning. Practitioners in robotics and AI may care about Zetta's approach as it could lead to more reliable and scalable physical intelligence.
This paper introduces a method to generate high-quality long-horizon trajectories for terminal agents, allowing them to complete complex tasks in a realistic and autonomous way. Practitioners in AI/ML research and development can benefit from this approach to improve the performance and reliability of autonomous systems.
This paper develops a system that can track both hands of a robot in complex, real-life situations, using demonstrations from humans. Practitioners might care because it could enable robots to perform tasks like assembly, cooking, or surgery with greater precision and flexibility.
This paper introduces TurboVLA, a new vision-language-action model that reduces computation and memory overhead by directly exchanging information between visual observations and language instructions, allowing for faster and more efficient robotic manipulation. Practitioners might care about this approach for building more efficient and effective VLA models.
This paper explores how AI can be applied across different stages of game development, from playing games to designing and testing them, and how to reuse capabilities across these stages. Practitioners might care about how to apply AI to improve game development efficiency and effectiveness.
This paper introduces a new way for robots to learn from each other's experiences, without needing human corrections, to improve their performance in real-world tasks. Practitioners might care because it could make robots more reliable and efficient in complex tasks.
This paper proposes a model-system framework called Long-WAM to improve real-time robot control by scaling the context of causal world-action models. A practitioner might care because it can help robots learn from their past actions and make better decisions in complex tasks.