7 citations · 16 across the 19 of their papers we have counts for
9 papers · 1 filter
HiRE: Hindsight Reward Editing for Policy Finetuning
Haoyi Niu, Zhengtao Han, Yufeng Ji +2
Pre-trained robot policies always require finetuning to adapt to specific environments. Reinforcement Learning (RL) offers high performance potential because it improves action opt…
A Recipe for Efficient Sim-to-Real Transfer in Manipulation with Online Imitation-Pretrained World Models
Yilin Wang, Shangzhe Li, Haoyi Niu +4
We are interested in solving the problem of imitation learning with a limited amount of real-world expert data. Existing offline imitation methods often struggle with poor data cov…
PhysiAgent: An Embodied Agent Framework in Physical World
Zhihao Wang, Jianxiong Li, Jinliang Zheng +6
Vision-Language-Action (VLA) models have achieved notable success but often struggle with limited generalizations. To address this, integrating generalized Vision-Language Models (…
Efficient Robotic Policy Learning via Latent Space Backward Planning
Dongxiu Liu, Haoyi Niu, Zhihao Wang +6
Current robotic planning methods often rely on predicting multi-frame images with full pixel details. While this fine-grained approach can serve as a generic world model, it introd…
xTED: Cross-Domain Adaptation via Diffusion-Based Trajectory Editing
Haoyi Niu, Qimao Chen, Tenglong Liu +5
Reusing pre-collected data from different domains is an appealing solution for decision-making tasks, especially when data in the target domain are limited. Existing cross-domain p…
DecisionNCE: Embodied Multimodal Representations via Implicit Preference Learning
Jianxiong Li, Jinliang Zheng, Yinan Zheng +9
Multimodal pretraining is an effective strategy for the trinity of goals of representation learning in autonomous robots: 1) extracting both local and global task progressions; 2)…
A Comprehensive Survey of Cross-Domain Policy Transfer for Embodied Agents
Haoyi Niu, Jianming Hu, Guyue Zhou +1
The burgeoning fields of robot learning and embodied AI have triggered an increasing demand for large quantities of data. However, collecting sufficient unbiased data from the targ…
A Versatile and Efficient Reinforcement Learning Framework for Autonomous Driving
Guan Wang, Haoyi Niu, Desheng Zhu +3
Heated debates continue over the best autonomous driving framework. The classic modular pipeline is widely adopted in the industry owing to its great interpretability and stability…
DR2L: Surfacing Corner Cases to Robustify Autonomous Driving via Domain Randomization Reinforcement Learning
Haoyi Niu, Jianming Hu, Zheyu Cui +1
How to explore corner cases as efficiently and thoroughly as possible has long been one of the top concerns in the context of deep reinforcement learning (DeepRL) autonomous drivin…