Accelerated Multi-Modal Motion Planning Using Context-Conditioned Diffusion Models

2025-10-16

AI Summary

Traditional motion planning methods suffer from poor generalization in high-dimensional spaces and complex environments, while existing diffusion-based approaches rely on specific sensors or single-environment training. To address these limitations, we propose CAMPD—a sensor-agnostic, context-conditioned diffusion model. CAMPD is the first to decouple classifier-free guidance from arbitrary contextual parameters, enabling flexible integration of diverse environmental cues. It employs an attention-enhanced U-Net architecture for end-to-end multimodal trajectory distribution modeling. Crucially, CAMPD achieves zero-shot transfer to unseen environments without fine-tuning. Evaluated on a 7-DoF robotic manipulator, it demonstrates significantly accelerated planning speed, high-quality trajectory generation, and robust cross-environment generalization—outperforming prior diffusion and classical planners in both efficiency and adaptability.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Intelligent Robots: Motion and Path Planning
• Multiagent Systems: Multiagent Planning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Classical methods in robot motion planning, such as sampling-based and optimization-based methods, often struggle with scalability towards higher-dimensional state spaces and complex environments. Diffusion models, known for their capability to learn complex, high-dimensional and multi-modal data distributions, provide a promising alternative when applied to motion planning problems and have already shown interesting results. However, most of the current approaches train their model for a single environment, limiting their generalization to environments not seen during training. The techniques that do train a model for multiple environments rely on a specific camera to provide the model with the necessary environmental information and therefore always require that sensor. To effectively adapt to diverse scenarios without the need for retraining, this research proposes Context-Aware Motion Planning Diffusion (CAMPD). CAMPD leverages a classifier-free denoising probabilistic diffusion model, conditioned on sensor-agnostic contextual information. An attention mechanism, integrated in the well-known U-Net architecture, conditions the model on an arbitrary number of contextual parameters. CAMPD is evaluated on a 7-DoF robot manipulator and benchmarked against state-of-the-art approaches on real-world tasks, showing its ability to generalize to unseen environments and generate high-quality, multi-modal trajectories, at a fraction of the time required by existing methods.

Problem

• Solving robot motion planning in high-dimensional complex environments
• Overcoming limited generalization of diffusion models to unseen environments
• Eliminating dependency on specific sensors for environmental conditioning

Innovation

• Uses sensor-agnostic diffusion model for generalization
• Integrates attention mechanism in U-Net architecture
• Generates multi-modal trajectories faster than existing methods