5 papers
ZeroBot: Learning from Scratch in Minutes with Generative Real2Sim
Ivan Kapelyukh, Xiaohan Zhang, Stephen James +2
We present ZeroBot, a real2sim framework for learning a robot manipulation task from scratch in minutes under challenging conditions: zero human demonstrations, zero policy pre-tra…
Learning to unfold cloth: Scaling up world models to deformable object manipulation
Jack Rome, Stephen James, Subramanian Ramamoorthy
Learning to manipulate cloth is both a paradigmatic problem for robotic research and a problem of immediate relevance to a variety of applications ranging from assistive care to th…
Continuous Control with Coarse-to-fine Reinforcement Learning
Younggyo Seo, Jafar Uruç, Stephen James
Despite recent advances in improving the sample-efficiency of reinforcement learning (RL) algorithms, designing an RL algorithm that can be practically deployed in real-world envir…
Generative Image as Action Models
Mohit Shridhar, Yat Long Lo, Stephen James
Image-generation diffusion models have been fine-tuned to unlock new capabilities such as image-editing and novel view synthesis. Can we similarly unlock image-generation models fo…
Render and Diffuse: Aligning Image and Action Spaces for Diffusion-based Behaviour Cloning
Vitalis Vosylius, Younggyo Seo, Jafar Uruç +1
In the field of Robot Learning, the complex mapping between high-dimensional observations such as RGB images and low-level robotic actions, two inherently very different spaces, co…