Recent research on instructable agents has used memory-augmented Large Language Models (LLMs) as task planners, a technique that retrieves language-program examples relevant to the input instruction and uses them as in-context examples in the LLM prompt to improve the performance of the LLM in inferring the correct action and task plans. In this technical report, we extend the capabilities of HELPER, by expanding its memory with a wider array of examples and prompts, and by integrating additional APIs for asking questions. This simple expansion of HELPER into a shared memory enables the agent to work across the domains of executing plans from dialogue, natural language instruction following, active question asking, and commonsense room reorganization. We evaluate the agent on four diverse interactive visual-language embodied agent benchmarks: ALFRED, TEACh, DialFRED, and the Tidy Task. HELPER-X achieves few-shot, state-of-the-art performance across these benchmarks using a single agent, without requiring in-domain training, and remains competitive with agents that have undergone in-domain training.

扩展HELPER的记忆范围，整合额外的API来提问，使得代理可以跨多个领域进行计划执行、自然语言指令跟踪、主动提问和常识房间重组，HELPER-X在四个不同的互动视觉-语言体验代理基准测试中取得了少量训练和最新框架表现，与接受过特定领域培训的代理保持一定的竞争力。

HELPER-X：基于记忆增强语言模型的统一可指导体现代理人解决四个交互式视觉语言领域