SeedPolicy: Horizon Scaling via Self-Evolving Diffusion Policy for Robot Manipulation
Imitation Learning (IL) enables robots to acquire manipulation skills from expert demonstrations. Diffusion Policy (DP) models multi-modal expert behaviors but suffers performance degradation as observation horizons increase, limiting long-horizon manipulation. We propose Self-Evolving Gated Attention (SEGA), a temporal module that maintains a time-evolving latent state via gated attention, enabling efficient recurrent updates that compress long-horizon observations into a fixed-size representation while filtering irrelevant temporal information. Integrating SEGA into DP yields Self-Evolving Diffusion Policy (SeedPolicy), which resolves the temporal modeling bottleneck and enables scalable horizon extension with moderate overhead. On the RoboTwin 2.0 benchmark with 50 manipulation tasks, SeedPolicy outperforms DP and other IL baselines. Averaged across both CNN and Transformer backbones, SeedPolicy achieves 36.8% relative improvement in clean settings and 169% relative improvement in randomized challenging settings over the DP. Compared to vision-language-action models such as RDT with 1.2B parameters, SeedPolicy achieves competitive performance with one to two orders of magnitude fewer parameters, demonstrating strong efficiency and scalability. These results establish SeedPolicy as a state-of-the-art imitation learning method for long-horizon robotic manipulation. Code is available at: https://github.com/Youqiang-Gui/SeedPolicy.
69ad045f9893f58272f73ef2
Youqiang Gui
69ad045f9893f58272f73ef3
Yuxuan Zhou
claimed_verified
2026-03-09T09:40:09.368Z
6568a5f0677a71b8ab245f4d
/avatars/d1791df0a74973c6870eeaeeed7a5a23.svg
yuxuan zhou
zhouyuxuanyx
user
zhouyuxuanyx
69ad045f9893f58272f73ef4
Shen Cheng
69ad045f9893f58272f73ef5
Xinyang Yuan
69ad045f9893f58272f73ef6
Haoqiang Fan
69ad045f9893f58272f73ef7
Peng Cheng
69ad045f9893f58272f73ef8
Shuaicheng Liu
SeedPolicy: Horizon Scaling via Self-Evolving Diffusion Policy for Robot Manipulation
Imitation Learning (IL) enables robots to acquire manipulation skills from expert demonstrations. Diffusion Policy (DP) models multi-modal expert behaviors but suffers performance degradation as observation horizons increase, limiting long-horizon manipulation. We propose Self-Evolving Gated Attention (SEGA), a temporal module that maintains a time-evolving latent state via gated attention, enabling efficient recurrent updates that compress long-horizon observations into a fixed-size representation while filtering irrelevant temporal information. Integrating SEGA into DP yields Self-Evolving Diffusion Policy (SeedPolicy), which resolves the temporal modeling bottleneck and enables scalable horizon extension with moderate overhead. On the RoboTwin 2.0 benchmark with 50 manipulation tasks, SeedPolicy outperforms DP and other IL baselines. Averaged across both CNN and Transformer backbones, SeedPolicy achieves 36.8% relative improvement in clean settings and 169% relative improvement in randomized challenging settings over the DP. Compared to vision-language-action models such as RDT with 1.2B parameters, SeedPolicy achieves competitive performance with one to two orders of magnitude fewer parameters, demonstrating strong efficiency and scalability. These results establish SeedPolicy as a state-of-the-art imitation learning method for long-horizon robotic manipulation. Code is available at: https://github.com/Youqiang-Gui/SeedPolicy.
Self-Evolving Gated Attention enables efficient temporal modeling in diffusion policies for long-horizon robotic manipulation, achieving superior performance with reduced computational requirements.
imitation learning diffusion policy temporal module gated attention recurrent updates latent state long-horizon manipulation RoboTwin 2.0 SeedPolicy vision-language-action models parameter-efficient fine-tuning