Semantic Intelligence: Integrating GPT-4 with A Planning in Low-Cost Robotics (2505.01931v1)

Abstract: Classical robot navigation often relies on hardcoded state machines and purely geometric path planners, limiting a robot's ability to interpret high-level semantic instructions. In this paper, we first assess GPT-4's ability to act as a path planner compared to the A* algorithm, then present a hybrid planning framework that integrates GPT-4's semantic reasoning with A* on a low-cost robot platform operating on ROS2 Humble. Our approach eliminates explicit finite state machine (FSM) coding by using prompt-based GPT-4 reasoning to handle task logic while maintaining the accurate paths computed by A*. The GPT-4 module provides semantic understanding of instructions and environmental cues (e.g., recognizing toxic obstacles or crowded areas to avoid, or understanding low-battery situations requiring alternate route selection), and dynamically adjusts the robot's occupancy grid via obstacle buffering to enforce semantic constraints. We demonstrate multi-step reasoning for sequential tasks, such as first navigating to a resource goal and then reaching a final destination safely. Experiments on a Petoi Bittle robot with an overhead camera and Raspberry Pi Zero 2W compare classical A* against GPT-4-assisted planning. Results show that while A* is faster and more accurate for basic route generation and obstacle avoidance, the GPT-4-integrated system achieves high success rates (96-100%) on semantic tasks that are infeasible for pure geometric planners. This work highlights how affordable robots can exhibit intelligent, context-aware behaviors by leveraging LLM reasoning with minimal hardware and no fine-tuning.

Semantic Intelligence: Integrating GPT-4 with A* Planning in Low-Cost Robotics

The paper "Semantic Intelligence: Integrating GPT-4 with A* Planning in Low-Cost Robotics" explores a novel robotic navigation framework, leveraging the semantic reasoning capabilities of LLMs, specifically GPT-4, in conjunction with the classical A* path planning algorithm. The primary focus is to address the inadequacies of purely geometric planners in interpreting high-level semantic instructions by integrating GPT-4’s advanced reasoning capabilities to endow low-cost robotic platforms with semantic intelligence.

Overview of Approach

This research introduces a hybrid planning system aimed at affordable robots, demonstrating that sophisticated AI can be utilized without significant additional hardware investments. The paper assesses GPT-4’s efficacy as a path planner compared to A*, and subsequently evaluates a collaborative framework where GPT-4 and A* are integrated within the Robot Operating System (ROS2) environment. Here, GPT-4 provides semantic understanding for task logic and environmental descriptors, while A* ensures precise route computation.

The integrated system eliminates finite state machine (FSM) coding, using GPT-4’s prompt-based reasoning to interpret instructions and specifications dynamically. It can adjust the robot's occupancy grid to enforce semantic constraints, such as recognizing adverse obstacles (e.g., toxic regions) or selecting paths that prioritize safety scenarios, like charging when the battery is low.

Experimentation and Results

The research involves extensive experimentation with a Petoi Bittle robot, applying the hybrid planning system to various scenarios with incremental complexity. These include simple geometric navigation tasks where classical A* path planning was benchmarked against GPT-4’s capabilities, followed by more complex semantic interpretation tasks and sequential reasoning challenges.

1. Geometric Navigation Tests: Classical A* proved faster and more reliable for basic route generation and obstacle avoidance due to its optimality and established robustness in geometric tasks. However, GPT-4 exhibited notable competence when tasked with route planning, albeit with increased planning time and complexity due to the LLM's cognitive processing.
2. Semantic Interpretation: In scenarios requiring semantic understanding, such as avoiding toxic obstacles or selecting charging stations due to low battery, GPT-4 achieved high success rates (90–100%), overcoming the limitations of classical geometrical planners. This underscores the ability of GPT-4 to comprehend and act upon high-level instructions that traditional planners cannot.
3. Sequential Reasoning: The system demonstrated effective multi-step reasoning, executing tasks requiring sequential goals while dynamically adjusting safety buffers according to the environmental context. This ability confirms GPT-4’s strength in managing complex task logic and adapting path strategies in real-time.

Implications and Future Directions

The research highlights significant implications, revealing how integrating LLMs like GPT-4 with classical planning can enhance robotic autonomy and intelligence at minimal cost. Some critical benefits include reduced manual coding by eliminating FSM in behavior logic and facilitating a flexible robotic system adaptable through prompt engineering.

Future Prospects involve further refining LLM strategies to enhance real-time performance and safety, adapting smaller, faster models for embedded environments, or locally executing computational tasks to reduce dependence on cloud-based services. There is potential exploration in extending the framework to handle broader multimodal inputs, enhancing robot sensory capabilities.

Overall, the paper convincingly demonstrates the transformative potential of semantic intelligence in robotics, powered by LLMs in conjunction with established classical algorithms, setting a foundation for future advancements in affordable, intelligent robotic systems.