1 citations · 2 across the 12 of their papers we have counts for
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Learning a Thousand Tasks in a Day
Kamil Dreczkowski, Pietro Vitiello, Vitalis Vosylius +1
Humans are remarkably efficient at learning tasks from demonstrations, but today's imitation learning methods for robot manipulation often require hundreds or thousands of demonstr…
Observer-Actor: Active Vision Imitation Learning with Sparse-View Gaussian Splatting
Yilong Wang, Cheng Qian, Ruomeng Fan +1
We propose Observer Actor (ObAct), a novel framework for active vision imitation learning in which the observer moves to optimal visual observations for the actor. We study ObAct o…
Neural Stochastic Flows: Solver-Free Modelling and Inference for SDE Solutions
Naoki Kiyohara, Edward Johns, Yingzhen Li
Stochastic differential equations (SDEs) are well suited to modelling noisy and irregularly sampled time series found in finance, physics, and machine learning. Traditional approac…
Learning in ImaginationLand: Omnidirectional Policies through 3D Generative Models (OP-Gen)
Yifei Ren, Edward Johns
Recent 3D generative models, which are capable of generating full object shapes from just a few images, now open up new opportunities in robotics. In this work, we show that 3D gen…