Multi-agent environments involve multiple embodied agents interacting with each other — cooperating on tasks, competing for resources, or simply sharing the same physical space. This includes human-robot teams working together as well as robot-robot coordination, where several robots must operate safely and efficiently in the…
Continue reading...Navigation and mapping allow embodied agents to move purposefully through environments while building and updating spatial representations. This includes localization, path planning, obstacle avoidance, and semantic understanding of spaces. SLAM (Simultaneous Localization and Mapping), visual odometry, and learned navigation policies are common. Integration with semantics…
Continue reading...Object manipulation involves detecting, grasping, moving, and using objects with precision and adaptability in the real world. It is one of the most important skills for embodied AGI because so many useful tasks — from making coffee to assembling furniture or helping with household chores…
Continue reading...Physics interaction refers to how embodied agents engage with the real physical properties of objects and environments — forces, collisions, friction, deformation, gravity, inertia, and material dynamics. Understanding and cleverly exploiting these laws is fundamental to competent real-world behavior. Without it, even simple actions like…
Continue reading...Reinforcement learning (RL) is a machine learning paradigm where an agent learns which actions to take by interacting with an environment and trying to maximize cumulative rewards through trial and error. The agent receives positive rewards for desirable outcomes and negative rewards (or penalties) for…
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