Nicklas Hansen

PhD student@University of California San Diego·美国·OpenReview

研究方向

reinforcement learning · generalization · adaptation · model-based reinforcement learning · world models · visual reinforcement learning · self-supervised learning · representation learning · deep learning · natural language processing

6.0

Multi-Stage Manipulation with Demonstration-Augmented Reward, Policy, and World Model Learning

ICML 2025Poster

二作

6.0

PWM: Policy Learning with Multi-Task World Models

合作者 (20)

Nicklas Hansen

Learning Massively Multitask World Models for Continuous Control

TIPS: Turn-level Information-Potential Reward Shaping for Search-Augmented LLMs

Leveraging Physics-Based Models for Rapid Adaptation in Reinforcement Learning

Towards Policy-Aware World Models

Hierarchical World Models as Visual Whole-Body Humanoid Controllers

Multi-Stage Manipulation with Demonstration-Augmented Reward, Policy, and World Model Learning

PWM: Policy Learning with Multi-Task World Models