Robot learning is often difficult due to the expense of gathering data. ...
In this paper, we investigate in detail the structures of the variationa...
We study the sequential decision-making problem of allocating a limited
...
The state-of-the-art multi-agent reinforcement learning (MARL) methods h...
Synchronizing decisions across multiple agents in realistic settings is
...
Centralized Training for Decentralized Execution, where training is done...
Policy gradient methods have become popular in multi-agent reinforcement...
Centralized Training for Decentralized Execution, where agents are train...
Based on hierarchical partitions, we provide the construction of Haar-ty...
In real-world multi-robot systems, performing high-quality, collaborativ...
In many real-world multi-robot tasks, high-quality solutions often requi...
Traditional methods for achieving high localization accuracy on tactile
...
Achieving high spatial resolution in contact sensing for robotic manipul...
Fully wearable hand rehabilitation and assistive devices could extend
tr...
A key challenge in multi-robot and multi-agent systems is generating
sol...