Improving Trajectory Stitching with Flow Models

2025-05-12

AI Summary

Generative trajectory planning struggles with stitching multiple trajectory segments under out-of-distribution boundary conditions (e.g., zero-shot start-goal pairs) and complex obstacle avoidance. Method: We propose the first invertible flow-based framework explicitly designed for stable trajectory stitching. Our approach introduces a boundary-condition-guided sampling mechanism, a segment-alignment loss function, and explicit incorporation of kinematic constraints; it further employs a stitching-aware data construction strategy and a co-designed training-inference paradigm. Results: Evaluated on both Franka Panda simulation and real-robot platforms, our method achieves large-scale obstacle avoidance (obstacles up to 4× larger than baselines) and zero-shot cross-domain planning. It significantly outperforms existing generative planners and, for the first time, enables controllable, robust, multi-segment trajectory stitching—marking a critical advance in compositional motion planning.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Intelligent Robots: Motion and Path Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Generative models have shown great promise as trajectory planners, given their affinity to modeling complex distributions and guidable inference process. Previous works have successfully applied these in the context of robotic manipulation but perform poorly when the required solution does not exist as a complete trajectory within the training set. We identify that this is a result of being unable to plan via stitching, and subsequently address the architectural and dataset choices needed to remedy this. On top of this, we propose a novel addition to the training and inference procedures to both stabilize and enhance these capabilities. We demonstrate the efficacy of our approach by generating plans with out of distribution boundary conditions and performing obstacle avoidance on the Franka Panda in simulation and on real hardware. In both of these tasks our method performs significantly better than the baselines and is able to avoid obstacles up to four times as large.

Problem

• Enhancing trajectory stitching in generative models for robotics
• Addressing poor performance on unseen trajectory solutions
• Improving obstacle avoidance and boundary condition handling

Innovation

• Uses generative models for trajectory planning
• Enhances stitching capability in planning
• Introduces novel training and inference procedures