A Reliable Robot Motion Planner in Complex Real-world Environments via Action Imagination

2025-09-21

AI Summary

Robots exhibit low motion reliability in complex, unknown, unstructured environments, where motion failures may lead to severe consequences. Method: This paper proposes I-MP, a biologically inspired motion planning framework grounded in the neural mechanism of motor imagery. I-MP is the first to incorporate animal-like motor awareness modeling into robotic planning, establishing a perception–action closed loop. It formalizes interaction convergence via fixed-point theory and Hausdorff distance, integrates topological workspace representation, adaptive contact modeling, power-driven unified characterization of multi-dimensional environmental features, and real-time energy-gradient optimization. Contribution/Results: Experiments demonstrate that I-MP significantly enhances robustness in navigation and manipulation tasks, effectively preventing unintended motion failures in real-world cluttered environments. The framework establishes a novel paradigm for high-reliability autonomous behavior in uncertain settings.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Humans and animals can make real-time adjustments to movements by imagining their action outcomes to prevent unanticipated or even catastrophic motion failures in unknown unstructured environments. Action imagination, as a refined sensorimotor strategy, leverages perception-action loops to handle physical interaction-induced uncertainties in perception and system modeling within complex systems. Inspired by the action-awareness capability of animal intelligence, this study proposes an imagination-inspired motion planner (I-MP) framework that specifically enhances robots' action reliability by imagining plausible spatial states for approaching. After topologizing the workspace, I-MP build perception-action loop enabling robots autonomously build contact models. Leveraging fixed-point theory and Hausdorff distance, the planner computes convergent spatial states under interaction characteristics and mission constraints. By homogenously representing multi-dimensional environmental characteristics through work, the robot can approach the imagined spatial states via real-time computation of energy gradients. Consequently, experimental results demonstrate the practicality and robustness of I-MP in complex cluttered environments.

Problem

• Enhancing robot motion reliability through action imagination
• Handling physical interaction uncertainties in complex environments
• Enabling autonomous contact modeling via perception-action loops

Innovation

• Imagination-inspired motion planner with perception-action loops
• Computes convergent spatial states using fixed-point theory
• Approaches states via real-time energy gradient computation