We propose and demonstrate the task of giving natural language summaries of
the actions of a robotic agent in a virtual environment. We explain why such a
task is important, what makes it difficult, and discuss how it might be
addressed. To encourage others to work on this, we show how a popular existing
dataset that matches robot actions with natural language descriptions designed
for an instruction following task can be repurposed to serve as a training
ground for robot action summarization work. We propose and test several methods
of learning to generate such summaries, starting from either egocentric video
frames of the robot taking actions or intermediate text representations of the
actions used by an automatic planner. We provide quantitative and qualitative
evaluations of our results, which can serve as a baseline for future work.

我们提出并展示了在虚拟环境中给出机器人行为的自然语言摘要的任务，针对该任务，我们提出并测试了几种生成这样摘要的方法，并设计了一个基准模型，可作为未来工作的基础。

用自然语言总结虚拟机器人的过去动作

Summarizing a virtual robot's past actions in natural language

We describe and evaluate the algorithmic techniques that are used in the FF
planning system. Like the HSP system, FF relies on forward state space search,
using a heuristic that estimates goal distances by ignoring delete lists.
Unlike HSP's heuristic, our method does not assume facts to be independent. We
introduce a novel search strategy that combines hill-climbing with systematic
search, and we show how other powerful heuristic information can be extracted
and used to prune the search space. FF was the most successful automatic
planner at the recent AIPS-2000 planning competition. We review the results of
the competition, give data for other benchmark domains, and investigate the
reasons for the runtime performance of FF compared to HSP.

基于前向状态空间搜索的启发式搜索算法是实现 FF 规划系统的主要技巧，该系统因比 HSP 系统更具优越性能而赢得了最近 AIPS-2000 规划竞赛的冠军。