103 citations · 322 across the 32 of their papers we have counts for
5 papers · 1 filter
RLDG: Robotic Generalist Policy Distillation via Reinforcement Learning
Charles Xu, Qiyang Li, Jianlan Luo +1
Recent advances in robotic foundation models have enabled the development of generalist policies that can adapt to diverse tasks. While these models show impressive flexibility, th…
A Simulation Benchmark for Autonomous Racing with Large-Scale Human Data
Adrian Remonda, Nicklas Hansen, Ayoub Raji +4
Despite the availability of international prize-money competitions, scaled vehicles, and simulation environments, research on autonomous racing and the control of sports cars opera…
PWM: Policy Learning with Multi-Task World Models
Ignat Georgiev, Varun Giridhar, Nicklas Hansen +1
Reinforcement Learning (RL) has made significant strides in complex tasks but struggles in multi-task settings with different embodiments. World model methods offer scalability by…
Hierarchical World Models as Visual Whole-Body Humanoid Controllers
Nicklas Hansen, Jyothir S, Vlad Sobal +3
Whole-body control for humanoids is challenging due to the high-dimensional nature of the problem, coupled with the inherent instability of a bipedal morphology. Learning from visu…
A Recipe for Unbounded Data Augmentation in Visual Reinforcement Learning
Abdulaziz Almuzairee, Nicklas Hansen, Henrik I. Christensen
Q-learning algorithms are appealing for real-world applications due to their data-efficiency, but they are very prone to overfitting and training instabilities when trained from vi…