7 citations · 15 across the 9 of their papers we have counts for
10 papers
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…
Are Expressive Models Truly Necessary for Offline RL?
Guan Wang, Haoyi Niu, Jianxiong Li +3
Among various branches of offline reinforcement learning (RL) methods, goal-conditioned supervised learning (GCSL) has gained increasing popularity as it formulates the offline RL…
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…
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…
Stackelberg Driver Model for Continual Policy Improvement in Scenario-Based Closed-Loop Autonomous Driving
Haoyi Niu, Qimao Chen, Yingyue Li +2
The deployment of autonomous vehicles (AVs) has faced hurdles due to the dominance of rare but critical corner cases within the long-tail distribution of driving scenarios, which n…
Continual Driving Policy Optimization with Closed-Loop Individualized Curricula
Haoyi Niu, Yizhou Xu, Xingjian Jiang +1
The safety of autonomous vehicles (AV) has been a long-standing top concern, stemming from the absence of rare and safety-critical scenarios in the long-tail naturalistic driving d…
H2O+: An Improved Framework for Hybrid Offline-and-Online RL with Dynamics Gaps
Haoyi Niu, Tianying Ji, Bingqi Liu +7
Solving real-world complex tasks using reinforcement learning (RL) without high-fidelity simulation environments or large amounts of offline data can be quite challenging. Online R…
(Re)H2O: Autonomous Driving Scenario Generation via Reversely Regularized Hybrid Offline-and-Online Reinforcement Learning
Haoyi Niu, Kun Ren, Yizhou Xu +4
Autonomous driving and its widespread adoption have long held tremendous promise. Nevertheless, without a trustworthy and thorough testing procedure, not only does the industry str…
When to Trust Your Simulator: Dynamics-Aware Hybrid Offline-and-Online Reinforcement Learning
Haoyi Niu, Shubham Sharma, Yiwen Qiu +4
Learning effective reinforcement learning (RL) policies to solve real-world complex tasks can be quite challenging without a high-fidelity simulation environment. In most cases, we…
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…