Embodied lifelong learning for decision making: Opportunities brought on by modularity – Learning and Intelligent Systems Group

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Embodied intelligence is the ultimate lifelong learning problem. If you had a robot in your home, you would likely ask it to do all sorts of varied chores, like setting the table for dinner, preparing lunch, and doing a load of laundry. The things you would ask it to do might also change over time, for example to use new appliances. You would want your robot to learn to do your chores and adapt to any changes quickly. As we will see, the fact that the learning agent is a robot transforms the lifelong learning problem, making it a) unique and b) tractable. Most significantly, compared to other domains like language or vision, we have plausible mechanisms for solving robotics problems by decomposing them—for example, task and motion planning (TAMP) methods break problems down into multi-step plans and multi-stage processing pipelines. These various forms of modularity will allow the robot to construct much simpler lifelong learning problems for itself and generalize compositionally, whil

Embodied lifelong learning for decision making: Opportunities brought on by modularity – Learning and Intelligent Systems Group Blog // The Latest News from LIS September 5, 2023 Embodied lifelong learning for decision making: Opportunities brought on by modularity Authors: Jorge Mendez Mendez Embodied intelligence is the ultimate lifelong learning problem. If you had a robot in your home, you would likely ask it to do all sorts of varied chores, like setting the table for dinner, preparing lunch, and doing a load of laundry. The things you would ask it to do might also change over time,

Embodied lifelong learning for decision making: Opportunities brought on by modularity – Learning and Intelligent Systems Group

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