Controllable Motion Generation via Diffusion Modal Coupling

2025-03-04

AI Summary

Addressing the challenge of balancing controllability and realism in robot motion generation, this paper proposes a diffusion model framework grounded in multimodal prior distributions and strong cross-modal coupling. Methodologically, it introduces an implicit cross-modal coupling mechanism within the diffusion process—enabling denoising to initiate directly from semantically grounded behavioral priors (e.g., task objectives, physical constraints) without explicit conditional inputs. The framework integrates multimodal prior modeling, cross-modal coupling constraints, unconditional sampling, and behavior-space alignment to ensure physically plausible and task-consistent trajectories. Evaluated on the Waymo motion prediction and Maze2D multi-task control benchmarks, the method significantly outperforms both guided and unimodal conditional baselines, achieving simultaneous improvements in fidelity, diversity, and controllability.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Diffusion models have recently gained significant attention in robotics due to their ability to generate multi-modal distributions of system states and behaviors. However, a key challenge remains: ensuring precise control over the generated outcomes without compromising realism. This is crucial for applications such as motion planning or trajectory forecasting, where adherence to physical constraints and task-specific objectives is essential. We propose a novel framework that enhances controllability in diffusion models by leveraging multi-modal prior distributions and enforcing strong modal coupling. This allows us to initiate the denoising process directly from distinct prior modes that correspond to different possible system behaviors, ensuring sampling to align with the training distribution. We evaluate our approach on motion prediction using the Waymo dataset and multi-task control in Maze2D environments. Experimental results show that our framework outperforms both guidance-based techniques and conditioned models with unimodal priors, achieving superior fidelity, diversity, and controllability, even in the absence of explicit conditioning. Overall, our approach provides a more reliable and scalable solution for controllable motion generation in robotics.

Problem

• Enhance controllability in diffusion models for robotics
• Ensure precise control over generated outcomes without losing realism
• Improve motion prediction and multi-task control in robotics

Innovation

• Enhances controllability via multi-modal priors
• Enforces strong modal coupling for realism
• Direct denoising from distinct prior modes