WorldLines: Benchmarking and Modeling Long-Horizon Stateful Embodied Agents
Quick Answer
WorldLines introduces a benchmark for long-horizon embodied agents, focusing on household assistance with memory capabilities.
Quick Take
It highlights challenges in partial observability and state management while proposing ObsMem, a framework for maintaining visibility-aware memories. Experiments show ObsMem as a stronger architecture for translating long-term memory into actionable plans.
Key Points
- WorldLines benchmarks long-horizon embodied agents for household assistance.
- ObsMem framework enhances visibility-aware memory management for agents.
- Experiments reveal challenges in translating long-term memory into actions.
- Focus on dynamic environments rather than traditional language-centric tasks.
- Addresses issues like overwritten world states and partial observability.
Paper Resources
Source Excerpt
To assist humans over extended periods in real homes, embodied agents must remember user routines, world states, and past interactions. Existing long-term memory benchmarks mainly evaluate language-centric retrieval and question answering, while embodied benchmarks often focus on short-horizon task execution without testing long-term memory use in dynamic environments. We introduce WorldLines, a project-driven benchmark for long-horizon embodied household assistance. It constructs temporally ext
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