Max Zuo

Max Zuo, Logan Schick, Matthew Gombolay et al.

Modern day computer games have extremely large state and action spaces. To detect bugs in these games' models, human testers play the games repeatedly to explore the game and find errors in the games. Such gameplay is exhaustive and time consuming. Moreover, since robotics simulators depend on similar methods of model specification and debugging, the problem of finding errors in the model is of interest to the robotics community to ensure robot behaviors and interactions are consistent in simulators. Previous methods have used reinforcement learning arXiv:2103.13798 and search based methods (Chang, 2019, (Chang, 2021) arXiv:1811.06962 including Rapidly-exploring Random Trees (RRT) to explore a game's state-action space to find bugs. However, such search and exploration based methods are not efficient at exploring the state-action space without a pre-defined heuristic. In this work we attempt to combine a human-tester's expertise in solving games, and the RRT's exhaustiveness to search a game's state space efficiently with high coverage. This paper introduces Cloning Assisted RRT (CA-RRT) to test a game through search. We compare our methods to two existing baselines: 1) a weighted-RRT as described by arXiv:1812.03125; 2) human demonstration seeded RRT as described by Chang et. al. We find CA-RRT is applicable to more game maps and explores more game states in fewer tree expansions/iterations when compared to the existing baselines. In each test, CA-RRT reached more states on average in the same number of iterations as weighted-RRT. In our tested environments, CA-RRT reached the same number of states as weighted-RRT by more than 5000 fewer iterations on average, almost a 50% reduction and applied to more scenarios than. Moreover, as a consequence of our first person behavior cloning approach, CA-RRT worked on unseen game maps than just seeding the RRT with human demonstrated states.

Max Zuo, Francisco Piedrahita Velez, Xiaochen Li et al.

Recent works have explored using language models for planning problems. One approach examines translating natural language descriptions of planning tasks into structured planning languages, such as the planning domain definition language (PDDL). Existing evaluation methods struggle to ensure semantic correctness and rely on simple or unrealistic datasets. To bridge this gap, we introduce \textit{Planetarium}, a benchmark designed to evaluate language models' ability to generate PDDL code from natural language descriptions of planning tasks. \textit{Planetarium} features a novel PDDL equivalence algorithm that flexibly evaluates the correctness of generated PDDL, along with a dataset of 145,918 text-to-PDDL pairs across 73 unique state combinations with varying levels of difficulty. Finally, we evaluate several API-access and open-weight language models that reveal this task's complexity. For example, 96.1\% of the PDDL problem descriptions generated by GPT-4o are syntactically parseable, 94.4\% are solvable, but only 24.8\% are semantically correct, highlighting the need for a more rigorous benchmark for this problem.