Influencing towards stable multi-agent interactions
Learning in multi-agent environments is difficult due to the non-stationarity introduced by an
opponent's or partner's changing behaviors. Instead of reactively adapting to the other …
opponent's or partner's changing behaviors. Instead of reactively adapting to the other …
Blind spot detection for safe sim-to-real transfer
Agents trained in simulation may make errors when performing actions in the real world due
to mismatches between training and execution environments. These mistakes can be …
to mismatches between training and execution environments. These mistakes can be …
[HTML][HTML] Deep Reinforcement Learning for sim-to-real policy transfer of VTOL-UAVs offshore docking operations
This paper proposes a novel Reinforcement Learning (RL) approach for sim-to-real policy
transfer of Vertical Take-Off and Landing Unmanned Aerial Vehicle (VTOL-UAV). The …
transfer of Vertical Take-Off and Landing Unmanned Aerial Vehicle (VTOL-UAV). The …
[PDF][PDF] Zero-shot skill composition and simulation-to-real transfer by learning task representations
Simulation-to-real transfer is an important strategy for making reinforcement learning
practical with real robots. Successful sim-to-real transfer systems have difficulty producing …
practical with real robots. Successful sim-to-real transfer systems have difficulty producing …
Conditionally Combining Robot Skills using Large Language Models
This paper combines two contributions. First, we introduce an extension of the Meta-World
benchmark, which we call" Language-World," which allows a large language model to …
benchmark, which we call" Language-World," which allows a large language model to …
Efficient multi-task learning via iterated single-task transfer
In order to be effective general purpose machines in real world environments, robots not
only will need to adapt their existing manipulation skills to new circumstances, they will need …
only will need to adapt their existing manipulation skills to new circumstances, they will need …
Hierarchical End-to-end Control Policy for Multi-degree-of-freedom Manipulators
CH Min, JB Song - International Journal of Control, Automation and …, 2022 - Springer
In recent years, several control policies for a multi-degree-of-freedom (DOF) manipulator
using deep reinforcement learning have been proposed. To avoid complexity, previous …
using deep reinforcement learning have been proposed. To avoid complexity, previous …
A simple approach to continual learning by transferring skill parameters
In order to be effective general purpose machines in real world environments, robots not
only will need to adapt their existing manipulation skills to new circumstances, they will need …
only will need to adapt their existing manipulation skills to new circumstances, they will need …
Scaling simulation-to-real transfer by learning a latent space of robot skills
We present a strategy for simulation-to-real transfer, which builds on recent advances in
robot skill decomposition. Rather than focusing on minimizing the simulation–reality gap, we …
robot skill decomposition. Rather than focusing on minimizing the simulation–reality gap, we …
Auto-conditioned recurrent mixture density networks for learning generalizable robot skills
Personal robots assisting humans must perform complex manipulation tasks that are
typically difficult to specify in traditional motion planning pipelines, where multiple objectives …
typically difficult to specify in traditional motion planning pipelines, where multiple objectives …