ProgPrompt: Generating Situated Robot Task Plans using Large Language Models

Everyday household tasks require both commonsense understanding of the world and situated knowledge about the words, which then need to be mapped to actions and world current environment. To create a task plan for "Make dinner," objects available to the agent. For example, if the LLM an agent needs common sense: object affordances, such as produced "reach in and pick up the jar of pickles," that that the stove and microwave can be used for heating; logical string would have to neatly map to an executable action like sequences of actions, such as an oven must be preheated before "pick up jar." A key component missing in LLM-based task food is added; and task relevance of objects and actions, planning is state feedback from the environment. The fridge such as heating and food are actions related to "dinner" in the in the house might not contain chicken, soda, or pickles, first place. However, this reasoning is infeasible without state but a high-level instruction "Make dinner" doesn't give us feedback. The agent needs to know what food is available in that world state information. Our work introduces situatedawareness the current environment, such as whether the freezer contains in LLM-based robot task planning.