Summary of Mrsteve: Instruction-following Agents in Minecraft with What-where-when Memory, by Junyeong Park et al.
MrSteve: Instruction-Following Agents in Minecraft with What-Where-When Memory
by Junyeong Park, Junmo Cho, Sungjin Ahn
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This paper proposes a novel low-level controller, MrSteve (Memory Recall Steve-1), which addresses the limitation of previous models by incorporating episodic memory. Specifically, it introduces Place Event Memory (PEM) to capture what, where, and when information from episodes. This enables efficient recall and navigation in long-horizon tasks. The approach combines with an Exploration Strategy and a Memory-Augmented Task Solving Framework, allowing agents to alternate between exploration and task-solving based on recalled events. The paper demonstrates improved task-solving and exploration efficiency compared to existing methods. The proposed framework is applied to embodied AI environments like Minecraft, showcasing the potential of LLM-augmented hierarchical approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a robot playing a game like Minecraft. It needs to remember what it did earlier in the game so it can make better decisions later on. This paper introduces a new way for robots to remember and learn from their experiences, called MrSteve. It’s like having a memory book that helps the robot recall important events and navigate through the game more efficiently. The researchers tested this approach and found that it helped the robot solve tasks much faster than before. They also released their code and demos online so others can try it out. |
Keywords
» Artificial intelligence » Recall
