1 citations · 2 across the 14 of their papers we have counts for
14 papers
Prioritized Rollouts for Efficient World Model-based Vision-Language-Action Policy Optimization
Yifei Sheng, Haoxiang Ren, Zhilong Zhang +8
Vision-Language-Action (VLA) models have emerged as a powerful paradigm for embodied intelligence, but fine-tuning them with reinforcement learning (RL) remains constrained by the…
Adversarial Imitation Learning with General Function Approximation: Theoretical Analysis and Practical Algorithms
Tian Xu, Zhilong Zhang, Zexuan Chen +3
Adversarial imitation learning (AIL), a prominent approach in imitation learning, has achieved significant practical success powered by neural network approximation. However, exist…
Speedup Patch: Learning a Plug-and-Play Policy to Accelerate Embodied Manipulation
Zhichao Wu, Junyin Ye, Zhilong Zhang +6
While current embodied policies exhibit remarkable manipulation skills, their execution remains unsatisfactorily slow as they inherit the tardy pacing of human demonstrations. Exis…
Towards Practical World Model-based Reinforcement Learning for Vision-Language-Action Models
Zhilong Zhang, Haoxiang Ren, Yihao Sun +6
Vision-Language-Action (VLA) models show strong generalization for robotic control, but finetuning them with reinforcement learning (RL) is constrained by the high cost and safety…
Skypilot: Fine-Tuning LLM with Physical Grounding for AAV Coverage Search
Zhongkai Chen, Yihao Sun, Chao Yan +3
Autonomous aerial vehicles (AAVs) have played a pivotal role in coverage operations and search missions. Recent advances in large language models (LLMs) offer promising opportuniti…
The Three Regimes of Offline-to-Online Reinforcement Learning
Lu Li, Tianwei Ni, Yihao Sun +1
Offline-to-online reinforcement learning (RL) has emerged as a practical paradigm that leverages offline datasets for pretraining and online interactions for fine-tuning. However,…
Planning with Unified Multimodal Models
Yihao Sun, Zhilong Zhang, Yang Yu +1
With the powerful reasoning capabilities of large language models (LLMs) and vision-language models (VLMs), many recent works have explored using them for decision-making. However,…
TeraSim: Uncovering Unknown Unsafe Events for Autonomous Vehicles through Generative Simulation
Haowei Sun, Xintao Yan, Zhijie Qiao +14
Traffic simulation is essential for autonomous vehicle (AV) development, enabling comprehensive safety evaluation across diverse driving conditions. However, traditional rule-based…
NeoRL-2: Near Real-World Benchmarks for Offline Reinforcement Learning with Extended Realistic Scenarios
Songyi Gao, Zuolin Tu, Rong-Jun Qin +3
Offline reinforcement learning (RL) aims to learn from historical data without requiring (costly) access to the environment. To facilitate offline RL research, we previously introd…
WHALE: Towards Generalizable and Scalable World Models for Embodied Decision-making
Zhilong Zhang, Ruifeng Chen, Junyin Ye +8
World models play a crucial role in decision-making within embodied environments, enabling cost-free explorations that would otherwise be expensive in the real world. To facilitate…
Provably and Practically Efficient Adversarial Imitation Learning with General Function Approximation
Tian Xu, Zhilong Zhang, Ruishuo Chen +2
As a prominent category of imitation learning methods, adversarial imitation learning (AIL) has garnered significant practical success powered by neural network approximation. Howe…
Any-step Dynamics Model Improves Future Predictions for Online and Offline Reinforcement Learning
Haoxin Lin, Yu-Yan Xu, Yihao Sun +6
Model-based methods in reinforcement learning offer a promising approach to enhance data efficiency by facilitating policy exploration within a dynamics model. However, accurately…
Episodic Return Decomposition by Difference of Implicitly Assigned Sub-Trajectory Reward
Haoxin Lin, Hongqiu Wu, Jiaji Zhang +3
Real-world decision-making problems are usually accompanied by delayed rewards, which affects the sample efficiency of Reinforcement Learning, especially in the extremely delayed c…
Model-based Reinforcement Learning with Multi-step Plan Value Estimation
Haoxin Lin, Yihao Sun, Jiaji Zhang +1
A promising way to improve the sample efficiency of reinforcement learning is model-based methods, in which many explorations and evaluations can happen in the learned models to sa…