RoboMemory: A Brain-inspired Multi-memory Agentic Framework for Lifelong Learning in Physical Embodied Systems
Mingcong Lei, Honghao Cai, Zezhou Cui, Liangchen Tan, Junkun Hong, Gehan Hu, Shuangyu Zhu, Yimou Wu, Shaohan Jiang, Ge Wang, Zhen Li, Shuguang Cui, Yiming Zhao, Yatong Han
2025-08-05
Summary
This paper talks about RoboMemory, a new system inspired by how the human brain works that helps robots learn continuously and plan their actions better while they operate in the real world.
What's the problem?
The problem is that robots need to learn new things all the time and remember past experiences to perform well, but it’s hard for existing systems to handle this long-term learning and planning efficiently without getting stuck or slow.
What's the solution?
RoboMemory solves this by combining different types of memory modules modeled after parts of the brain, like short-term and long-term memory, and using a planning system that lets robots update and use their memories quickly. This setup helps the robot learn from experience, remember important details, and plan steps to complete tasks in real life.
Why it matters?
This matters because it makes robots smarter and better at handling complex tasks over time, allowing them to work safely and effectively in real environments by remembering what they learned and improving with practice.
Abstract
RoboMemory, a brain-inspired multi-memory framework, enhances lifelong learning in physical robots by integrating cognitive neuroscience principles and achieving state-of-the-art performance in real-world tasks.