71 citations · 160 across the 17 of their papers we have counts for
5 papers · 1 filter
Clone Swarms: Learning to Predict and Control Multi-Robot Systems by Imitation
Siyu Zhou, Mariano Phielipp, Jorge A. Sefair +2
In this paper, we propose SwarmNet -- a neural network architecture that can learn to predict and imitate the behavior of an observed swarm of agents in a centralized manner. Teste…
Imitation Learning of Robot Policies by Combining Language, Vision and Demonstration
Simon Stepputtis, Joseph Campbell, Mariano Phielipp +2
In this work we propose a novel end-to-end imitation learning approach which combines natural language, vision, and motion information to produce an abstract representation of a ta…
On Training Flexible Robots using Deep Reinforcement Learning
Zach Dwiel, Madhavun Candadai, Mariano Phielipp
The use of robotics in controlled environments has flourished over the last several decades and training robots to perform tasks using control strategies developed from dynamical m…
Hierarchical Policy Learning is Sensitive to Goal Space Design
Zach Dwiel, Madhavun Candadai, Mariano Phielipp +1
Hierarchy in reinforcement learning agents allows for control at multiple time scales yielding improved sample efficiency, the ability to deal with long time horizons and transfera…
Goal-conditioned Imitation Learning
Yiming Ding, Carlos Florensa, Mariano Phielipp +1
Designing rewards for Reinforcement Learning (RL) is challenging because it needs to convey the desired task, be efficient to optimize, and be easy to compute. The latter is partic…