paint-brush
Navigation with Large Language Models: Semantic Guesswork as a Heuristic for Planning: Promptsby@heuristicsearch
105 reads

Navigation with Large Language Models: Semantic Guesswork as a Heuristic for Planning: Prompts

tldt arrow

Too Long; Didn't Read

In this paper we study how the “semantic guesswork” produced by language models can be utilized as a guiding heuristic for planning algorithms.
featured image - Navigation with Large Language Models: Semantic Guesswork as a Heuristic for Planning: Prompts
Aiding in the focused exploration of potential solutions. HackerNoon profile picture

This is paper is available on arxiv under CC 4.0 DEED license.

Authors:

(1) Dhruv Shah, UC Berkeley and he contributed equally;

(2) Michael Equi, UC Berkeley and he contributed equally;

(3) Blazej Osinski, University of Warsaw;

(4) Fei Xia, Google DeepMind;

(5) Brian Ichter, Google DeepMind;

(6) Sergey Levine, UC Berkeley and Google DeepMind.

B Prompts

B.1 Positive Prompt


Figure 7: LFG in an unseen apartment. The robot starts in the same starting location and environment as 5, and is tasked with finding an oven. LFG guides the robot towards the kitchen appliances, rather than the bedroom door, and successfully leads to the oven.


Figure 8: LFG in an unseen office building. The agent looks for a sink in an open-plan office building. Despite erroneous detections, the robot continues exploring the environment, with LFG guiding it towards frontiers containing appliances found in a cafe. The robot successfully finds the sink despite imperfect detections.



B.2 Negative Prompt