Intelligent and reliable task planning is a core capability for generalized robotics, requiring a descriptive domain representation that sufficiently models all object and state information for the scene. We present CLIMB, a continual learning framework for robot task planning that leverages foundation models and execution feedback to guide domain model construction. CLIMB can build a model from a natural language description, learn non-obvious predicates while solving tasks, and store that information for future problems. We demonstrate the ability of CLIMB to improve performance in common planning environments compared to baseline methods. We also develop the BlocksWorld++ domain, a simulated environment with an easily usable real counterpart, together with a curriculum of tasks with progressing difficulty for evaluating continual learning. Additional details and demonstrations for this system can be found at https://plan-with-climb.github.io/ .

翻译：智能可靠的任务规划是通用机器人的核心能力，这需要具备能够充分建模场景中所有对象与状态信息的描述性领域表示。本文提出CLIMB——一种用于机器人任务规划的持续学习框架，该框架利用基础模型与执行反馈来指导领域模型构建。CLIMB能够基于自然语言描述构建模型，在求解任务过程中学习非显式谓词，并将该信息存储用于未来问题求解。我们通过实验证明，相较于基线方法，CLIMB在常见规划环境中具有性能提升优势。同时，我们开发了BlocksWorld++领域——一个配备易用实体对应装置的仿真环境，并设计了难度递进的任务课程用于持续学习评估。该系统的更多细节与演示可见于 https://plan-with-climb.github.io/。