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2025

Diffusion Trajectory-guided Policy for Long-horizon Robot Manipulation

Fan, Shichao, Yang, Quantao, Liu, Yajie et al.

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Recently, Vision-Language-Action models (VLA) have advanced robot imitation learning, but high data collection costs and limited demonstrations hinder generalization and current imitation learning methods struggle in out-of-distribution scenarios, especially for long-horizon tasks.

A key challenge is how to mitigate compounding errors in imitation learning, which lead to cascading failures over extended trajectories.
To address these challenges, we propose the Diffusion Trajectory-guided Policy (DTP) framework, which generates 2D trajectories through a diffusion model to guide policy learning for long-horizon tasks.
By leveraging task-relevant trajectories, DTP provides trajectory-level guidance to reduce error accumulation.

Diffusion Trajectory-guided Policy for Long-horizon Robot Manipulation · Around

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