# Investigating the Effectiveness of a Socratic Chain-of-Thoughts Reasoning Method for Task Planning in Robotics, A Case Study

arXiv: https://arxiv.org/abs/2503.08174 · HTML: https://mlsanity.com/paper/2503.08174/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning.
- Builds on 8 of its 11 references that are in the map.
- First posted on arXiv 11 Mar 2025.

## Placed in

- LLM reasoning / [Spatial and embodied reasoning](https://mlsanity.com/lr/spatial-and-embodied-reasoning/)

## Builds on (most cited first, 8 of 8)

- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://mlsanity.com/paper/2305.14325/) (2023)
- [Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language](https://mlsanity.com/paper/2204.00598/) (2022)
- [ChatGPT for Robotics: Design Principles and Model Abilities](https://mlsanity.com/paper/2306.17582/) (2023)
- [Self-planning Code Generation with Large Language Models](https://mlsanity.com/paper/2303.06689/) (2023)
- [Towards Better Chain-of-Thought Prompting Strategies: A Survey](https://mlsanity.com/paper/2310.04959/) (2023)
- [Prompting Large Language Models With the Socratic Method](https://mlsanity.com/paper/2303.08769/) (2023)
- [Algorithmic Robust Forecast Aggregation](https://mlsanity.com/paper/2401.17743/) (2024)

## Abstract (arXiv)

Large language models (LLMs) have demonstrated unprecedented capability in reasoning with natural language. Coupled with this development is the emergence of embodied AI in robotics. Despite showing promise for verbal and written reasoning tasks, it remains unknown whether LLMs are capable of navigating complex spatial tasks with physical actions in the real world. To this end, it is of interest to investigate applying LLMs to robotics in zero-shot learning scenarios, and in the absence of fine-tuning - a feat which could significantly improve human-robot interaction, alleviate compute cost, and eliminate low-level programming tasks associated with robot tasks. To explore this question, we apply GPT-4(Omni) with a simulated Tiago robot in Webots engine for an object search task. We evaluate the effectiveness of three reasoning strategies based on Chain-of-Thought (CoT) sub-task list generation with the Socratic method (SocraCoT) (in order of increasing rigor): (1) Non-CoT/Non-SocraCoT, (2) CoT only, and (3) SocraCoT. Performance was measured in terms of the proportion of tasks successfully completed and execution time (N = 20). Our preliminary results show that when combined with chain-of-thought reasoning, the Socratic method can be used for code generation for robotic tasks that require spatial awareness. In extension of this finding, we propose EVINCE-LoC; a modified EVINCE method that could further enhance performance in highly complex and or dynamic testing scenarios.