Morphogenetic Assembly and Adaptive Control for Heterogeneous Modular Robots

2026-02-11

AI Summary

This work addresses the challenges of state-space explosion and real-time adaptive control under unknown configurations in large-scale reconfiguration of heterogeneous modular robots. To this end, the authors propose a closed-loop, automated framework spanning morphological construction to motion control. The framework employs a hierarchical planner that decouples discrete configuration search from continuous motion execution, integrating a bidirectional heuristic A* algorithm with type-aware penalties for efficient configuration planning. Furthermore, a configuration-agnostic annealed-variance MPPI controller is designed to achieve real-time motion tracking at 50 Hz. Simulations demonstrate that type-aware penalties substantially enhance robustness in heterogeneous scenarios, the proposed greedy heuristic outperforms the Hungarian method, and the annealed-variance MPPI significantly surpasses standard MPPI in both tracking accuracy and real-time performance.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

This paper presents a closed-loop automation framework for heterogeneous modular robots, covering the full pipeline from morphological construction to adaptive control. In this framework, a mobile manipulator handles heterogeneous functional modules including structural, joint, and wheeled modules to dynamically assemble diverse robot configurations and provide them with immediate locomotion capability. To address the state-space explosion in large-scale heterogeneous reconfiguration, we propose a hierarchical planner: the high-level planner uses a bidirectional heuristic search with type-penalty terms to generate module-handling sequences, while the low level planner employs A* search to compute optimal execution trajectories. This design effectively decouples discrete configuration planning from continuous motion execution. For adaptive motion generation of unknown assembled configurations, we introduce a GPU accelerated Annealing-Variance Model Predictive Path Integral (MPPI) controller. By incorporating a multi stage variance annealing strategy to balance global exploration and local convergence, the controller enables configuration-agnostic, real-time motion control. Large scale simulations show that the type-penalty term is critical for planning robustness in heterogeneous scenarios. Moreover, the greedy heuristic produces plans with lower physical execution costs than the Hungarian heuristic. The proposed annealing-variance MPPI significantly outperforms standard MPPI in both velocity tracking accuracy and control frequency, achieving real time control at 50 Hz. The framework validates the full-cycle process, including module assembly, robot merging and splitting, and dynamic motion generation.

Problem

• heterogeneous modular robots
• morphogenetic assembly
• adaptive control
• state-space explosion
• real-time motion control

Innovation

• hierarchical planning
• annealing-variance MPPI
• heterogeneous modular robots
• configuration-agnostic control
• morphogenetic assembly