Abstract
While natural systems often present collective intelligence that allows them to self-organize and adapt to changes, the equivalent is missing in most artificial systems. We explore the possibility of such a system in the context of cooperative 2D push manipulations using mobile robots. Although conventional works demonstrate potential solutions for the problem in restricted settings, they have computational and learning difficulties. More importantly, these systems do not possess the ability to adapt when facing environmental changes. In this work, we show that by distilling a planner derived from a differentiable soft-body physics simulator into an attention-based neural network, our multi-robot push manipulation system achieves better performance than baselines. In addition, our system also generalizes to configurations not seen during training and is able to adapt toward task completions when external turbulence and environmental changes are applied. Supplementary videos can be found on our project website: https://sites.google.com/view/ciom/home .
Tatsuya Matsushima
Project Researcher
My research interests include robot learning, robot system, and XR.
Jumpei Arima
I am an engineer working for a motor company in Japan.
Hiroki Furuta
Ph.D. student
Yutaka Matsuo
Professor
Shixiang Shane Gu
Visiting Associate Professor
Shixiang Shane Gu is a Senior Research Scientist at Google Brain, and a Visiting Associate Professor at the University of Tokyo, with research interests around (1) algorithmic problems in deep learning, reinforcement learning, robotics, and probabilistic machine learning, and (2) mastering a universal physics prior for continuous control. website