Learning to Live: A Step Toward Lifelong Embodied Intelligence
Learning to Live: A Step Toward Lifelong Embodied Intelligence
This research presents LIFE (Lifelong Intelligence from Experience), a framework enabling robots to continuously observe, selectively remember, and convert experience into new capabilities. The system utilizes RoboBeats for lightweight streaming condition tracking and Plastic Memory as a flat store preserving source evidence for future queries. Additionally, the Explore-Distill-Mount (EDM) process transforms problem-solving solutions into reusable tools. Experimental results indicate that under matched protocols, RoboBeats improves condition tracking over base models while retaining more useful evidence for memory question answering compared to uniform sampling. A case study involving a real robot replaying 24 hours of continuous video demonstrates the system's ability to build memory from daily experience and answer everyday questions.
The paper introduces LIFE, a framework combining RoboBeats for event tracking and Plastic Memory for evidence retention to enable lifelong learning in embodied agents, validated via a small-scale case study replaying 24 hours of real robot video.
Source: arXiv Robotics — research abstracts · Read original article ↗
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g into validated, reusable tools and skills. Under matched protocols, RoboBeats substantially improves condition tracking over its base models and, at the same writing budget, retains more useful evidence for memory question answering than uniform sampling. A case study of limited scale, replaying 24 hours of continuous video recorded by a real robot, illustrates how the integrated system builds memory from a day of
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g into validated, reusable tools and skills. Under matched protocols, RoboBeats substantially improves condition tracking over its base models and, at the same writing budget, retains more useful evidence for memory question answering than uniform sampling. A case study of limited scale, replaying 24 hours of continuous video recorded by a real robot, illustrates how the integrated system builds memory from a day of
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experience and uses it to answer everyday questions. LIFE takes a step toward robots that become more familiar with their surroundings and more capable through experience: learning not only to complete tasks, but to live in the world.
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arXiv:2611.00180v1 Announce Type: new Abstract: Embodied agents are usually organized around individual tasks, yet the physical world keeps changing between tasks and during execution. We introduce LIFE (Lifelong Intelligence from Experience), a framework in which a robot observes continuously, remembers selectively, and turns experience into new capabilities. RoboBeats, a family of lightweight streaming gates, track
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s conditions set by the agent and discovers new events, deciding when an observation warrants a response or a memory write. Selected observations enter Plastic Memory, a flat store of images and structured records that keeps the source evidence, so new fields and indexes can be added when later questions need them. When existing tools fall short, Explore-Distill-Mount (EDM) turns solutions found during problem solvin
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Learning to Live: A Step Toward Lifelong Embodied Intelligence
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Source：arXiv Robotics — research abstracts · arxiv.org