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ALAN : Autonomously Exploring Robotic Agents in the Real World
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ALAN : Autonomously Exploring Robotic Agents in the Real World
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ALAN : Autonomously Exploring Robotic Agents in the Real World
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2026-02-23 18:51:56
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ALAN : Autonomously Exploring Robotic Agents in the Real World ALAN : Autonomously Exploring Robotic Agents in the Real World Russell Mendonca Shikhar Bahl Deepak Pathak Carnegie Mellon University ICRA 2023 Paper arXiv Video Summary Abstract Robotic agents that operate autonomously in the real world need to continuously explore their environment and learn from the data collected, with minimal human supervision. While it is possible to build agents that can learn in such a manner without supervision, current methods struggle to scale to the real world. Thus, we propose ALAN, an autonomously exploring robotic agent, that can perform many tasks in the real world with little training and interaction time. This is enabled by measuring environment change, which reflects object movement and ignores changes in the robot position. We use this metric directly as an environment-centric signal, and also maximize the uncertainty of predict...
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