no code implementations • 7 Apr 2024 • Xudong Yu, Chenjia Bai, Haoran He, Changhong Wang, Xuelong Li
Sequential decision-making is desired to align with human intents and exhibit versatility across various tasks.
no code implementations • 22 Feb 2024 • Haoran He, Chenjia Bai, Ling Pan, Weinan Zhang, Bin Zhao, Xuelong Li
In the fine-tuning stage, we harness the imagined future videos to guide low-level action learning trained on a limited set of robot data.
1 code implementation • 2 Nov 2023 • Zhengbang Zhu, Hanye Zhao, Haoran He, Yichao Zhong, Shenyu Zhang, Haoquan Guo, Tingting Chen, Weinan Zhang
Diffusion models surpass previous generative models in sample quality and training stability.
1 code implementation • NeurIPS 2023 • Haoran He, Chenjia Bai, Kang Xu, Zhuoran Yang, Weinan Zhang, Dong Wang, Bin Zhao, Xuelong Li
Specifically, we propose Multi-Task Diffusion Model (\textsc{MTDiff}), a diffusion-based method that incorporates Transformer backbones and prompt learning for generative planning and data synthesis in multi-task offline settings.
1 code implementation • 29 May 2023 • Haoran He, Chenjia Bai, Hang Lai, Lingxiao Wang, Weinan Zhang
In this paper, we propose a novel single-stage privileged knowledge distillation method called the Historical Information Bottleneck (HIB) to narrow the sim-to-real gap.
no code implementations • 28 May 2023 • Kang Xu, Chenjia Bai, Shuang Qiu, Haoran He, Bin Zhao, Zhen Wang, Wei Li, Xuelong Li
Leveraging learned strategies in unfamiliar scenarios is fundamental to human intelligence.