BeSimulator: A Large Language Model Powered Text-based Behavior Simulator

2024-09-24

arXiv.org

AI Summary

Traditional robotic simulators suffer from high computational overhead, low efficiency, and poor generalizability due to their reliance on physics-based modeling and photorealistic rendering. This paper proposes a text-based behavioral simulation paradigm tailored for behavior-logic verification, establishing a lightweight, generalizable, and long-horizon semantic-level simulation framework that eschews physical rendering constraints and instead focuses on action feasibility and state-transition validity. We innovatively introduce the “Consider–Decide–Capture–Shift” (CoBS) behavioral chain, augment code-driven reasoning to enhance numerical reliability, and integrate a reflective iterative mechanism for dynamic simulation refinement. Evaluated on our newly constructed behavior-tree benchmark, BTSIMBENCH, our method achieves 14.7%–26.6% improvements over baselines, significantly enhancing long-term behavioral consistency and logical robustness in complex scenarios.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Intelligent Robots: Behavior Learning & Control

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Traditional robot simulators focus on physical process modeling and realistic rendering, often suffering from high computational costs, inefficiencies, and limited adaptability. To handle this issue, we propose Behavior Simulation in robotics to emphasize checking the behavior logic of robots and achieving sufficient alignment between the outcome of robot actions and real scenarios. In this paper, we introduce BeSimulator, a modular and novel LLM-powered framework, as an attempt towards behavior simulation in the context of text-based environments. By constructing text-based virtual environments and performing semantic-level simulation, BeSimulator can generalize across scenarios and achieve long-horizon complex simulation. Inspired by human cognition processes, it employs a"consider-decide-capture-transfer"methodology, termed Chain of Behavior Simulation, which excels at analyzing action feasibility and state transitions. Additionally, BeSimulator incorporates code-driven reasoning to enable arithmetic operations and enhance reliability, as well as integrates reflective feedback to refine simulation. Based on our manually constructed behavior-tree-based simulation benchmark BTSIMBENCH, our experiments show a significant performance improvement in behavior simulation compared to baselines, ranging from 14.7% to 26.6%.

Problem

• Simulating robot behavior logic efficiently without physical modeling
• Enhancing simulation adaptability and generalization across text-based scenarios
• Analyzing action feasibility and state transitions through cognitive-inspired processes

Innovation

• LLM-powered text-based behavior simulation framework
• Chain of Behavior Simulation four-phase cognitive process
• Code-driven reasoning with reflective feedback enhancement