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 …

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 …

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 …

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 …

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 …

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 …

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 …

Delayed Markov decision processes fulfill the Markov property by augmenting the state
space of agents with a finite time window of recently committed actions. In reliance with …

Personal robots assisting humans must perform complex manipulation tasks that are
typically difficult to specify in traditional motion planning pipelines, where multiple objectives …

Skill acquisition is among the most remarkable aspects of human intelligence. It involves
discovering purposeful behavioural modules, retaining them as skills, honing them through …