Search Results for author:
Found 6 papers, 1 papers with code
Zero-Shot Generalization of Vision-Based RL Without Data Augmentation
Specifically, we revisit the role of latent disentanglement in RL and show how combining it with a model of associative memory achieves zero-shot generalization on difficult task variations without relying on data augmentation.
Quality Diversity for Robot Learning: Limitations and Future Directions
Quality Diversity (QD) has shown great success in discovering high-performing, diverse policies for robot skill learning.
Collision Avoidance and Navigation for a Quadrotor Swarm Using End-to-end Deep Reinforcement Learning
In this work, we propose an end-to-end DRL approach to control quadrotor swarms in environments with obstacles.
QuadSwarm: A Modular Multi-Quadrotor Simulator for Deep Reinforcement Learning with Direct Thrust Control
In addition to speed, such simulators need to model the physics of the robots and their interaction with the environment to a level acceptable for transferring policies learned in simulation to reality.
Proximal Policy Gradient Arborescence for Quality Diversity Reinforcement Learning
Training generally capable agents that thoroughly explore their environment and learn new and diverse skills is a long-term goal of robot learning.
Augmented Reality for Human-Swarm Interaction in a Swarm-Robotic Chemistry Simulation
We present a method to register individual members of a robotic swarm in an augmented reality display while showing relevant information about swarm dynamics to the user that would be otherwise hidden.
