Summary of Optimus-1: Hybrid Multimodal Memory Empowered Agents Excel in Long-horizon Tasks, by Zaijing Li et al.
Optimus-1: Hybrid Multimodal Memory Empowered Agents Excel in Long-Horizon Tasks
by Zaijing Li, Yuquan Xie, Rui Shao, Gongwei Chen, Dongmei Jiang, Liqiang Nie
First submitted to arxiv on: 7 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A long-standing goal in artificial intelligence is to build a general-purpose agent capable of completing various tasks in an open world. Current agents have made significant progress, but they struggle with long-horizon tasks due to the lack of necessary knowledge and experience. This paper proposes the Hybrid Multimodal Memory module to address these challenges. The module transforms knowledge into Hierarchical Directed Knowledge Graphs and summarizes historical information into Abstracted Multimodal Experience Pools, allowing agents to learn from world knowledge and in-context experiences. A multimodal agent, Optimus-1, is constructed with a Knowledge-guided Planner and Experience-Driven Reflector, demonstrating improved planning and reflection for long-horizon tasks in Minecraft. Experimental results show that Optimus-1 outperforms existing agents on challenging task benchmarks and achieves near human-level performance on many tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine having an AI agent that can learn from experiences and make decisions like a human. This is the goal of artificial intelligence, but current agents struggle with long-term tasks because they don’t have enough information to guide them. This paper proposes a new way for agents to learn and make decisions by using more knowledge and experience. The idea is to create an agent that can plan ahead and reflect on its past experiences to make better choices. In this case, the agent was tested in a game called Minecraft and showed remarkable results, performing tasks as well as humans do. |
