# Bootstrapping Human-Like Planning via LLMs

arXiv: https://arxiv.org/abs/2506.22604 · HTML: https://mlsanity.com/paper/2506.22604/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Self-evolving LLM agents, one of 13 directions in Synthetic data and self-improvement for LLMs.
- Builds on 13 of its 36 references that are in the map.
- First posted on arXiv 27 Jun 2025.

## Placed in

- Synthetic data and self-improvement for LLMs / [Self-evolving LLM agents](https://mlsanity.com/synth/self-evolving-llm-agents/)

## Builds on (most cited first, 13 of 13)

- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks](https://mlsanity.com/paper/1912.01734/) (2019)
- [LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models](https://mlsanity.com/paper/2212.04088/) (2022)
- [VirtualHome: Simulating Household Activities via Programs](https://mlsanity.com/paper/1806.07011/) (2018)
- [Generalized Planning in PDDL Domains with Pretrained Large Language Models](https://mlsanity.com/paper/2305.11014/) (2023)
- [Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciences](https://mlsanity.com/paper/1712.00547/) (2017)
- [Planning in Strawberry Fields: Evaluating and Improving the Planning and Scheduling Capabilities of LRM o1](https://mlsanity.com/paper/2410.02162/) (2024)
- [Cocobo: Exploring Large Language Models as the Engine for End-User Robot Programming](https://mlsanity.com/paper/2407.20712/) (2024)
- [Goal-Oriented End-User Programming of Robots](https://mlsanity.com/paper/2403.13988/) (2024)
- [Situated Live Programming for Human-Robot Collaboration](https://mlsanity.com/paper/2108.03592/) (2021)
- [Crowdsourcing Task Traces for Service Robotics](https://mlsanity.com/paper/2403.14014/) (2024)
- [ImageInThat: Manipulating Images to Convey User Instructions to Robots](https://mlsanity.com/paper/2503.15500/) (2025)

## Abstract (arXiv)

Robot end users increasingly require accessible means of specifying tasks for robots to perform. Two common end-user programming paradigms include drag-and-drop interfaces and natural language programming. Although natural language interfaces harness an intuitive form of human communication, drag-and-drop interfaces enable users to meticulously and precisely dictate the key actions of the robot's task. In this paper, we investigate the degree to which both approaches can be combined. Specifically, we construct a large language model (LLM)-based pipeline that accepts natural language as input and produces human-like action sequences as output, specified at a level of granularity that a human would produce. We then compare these generated action sequences to another dataset of hand-specified action sequences. Although our results reveal that larger models tend to outperform smaller ones in the production of human-like action sequences, smaller models nonetheless achieve satisfactory performance.