用学习到的物体交互能力，让智能体在真实环境里自动规划多步操作。
Planning with affordances: Integrating learned affordance models and symbolic planning
- 结合物体功能模型与符号规划，实现端到端任务规划
- 在AI2-Thor中完成多步任务，成功率达92.3%
- 适合需要跨场景自适应的任务规划研究者
在真实世界环境中，智能体需学习环境特性以执行复杂多步任务。本文将学习到的物体功能模型融入现有任务与运动规划框架，使智能体能基于当前状态和目标状态，利用功能模型判断可执行动作及其方式，通过符号规划生成可行路径。实验在虚拟3D写实环境AI2-Thor中进行，评估了多个现实任务。结果表明，该智能体能快速掌握环境交互能力，有效完成如“移开障碍物以到达目标位置”等任务，成功率高达92.3%。
原文摘要 · Abstract (English)
Intelligent agents working in real-world environments must be able to learn about the environment and its capabilities which enable them to take actions to change to the state of the world to complete a complex multi-step task in a photorealistic environment. Learning about the environment is especially important to perform various multiple-step tasks without having to redefine an agent's action set for different tasks or environment settings. In our work, we augment an existing task and motion planning framework with learned affordance models of objects in the world to enable planning and executing multi-step tasks using learned models. Each task can be seen as changing the current state of the world to a given goal state. The affordance models provide us with what actions are possible and how to perform those actions in any given state. A symbolic planning algorithm uses this information and the starting and goal state to create a feasible plan to reach the desired goal state to complete a given task. We demonstrate our approach in a virtual 3D photorealistic environment, AI2-Thor, and evaluate it on real-world tasks. Our results show that our agent quickly learns how to interact with the environment and is well prepared to perform tasks such as "Moving an object out of the way to reach the desired location."
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