Deep Reinforcement Learning (DRL) is emerging as a promising approach to generate
adaptive behaviors for robotic platforms. However, a major drawback of using DRL is the …

Recent advances in deep reinforcement learning (deep RL) enable researchers to solve
challenging control problems, from simulated environments to real-world robotic tasks …

Applying Deep Reinforcement Learning (DRL) to complex tasks in the field of robotics has
proven to be very successful in the recent years. However, most of the publications focus …

This paper describes a deep reinforcement learning (DRL) approach that won Phase 1 of
the Real Robot Challenge (RRC) 2021, and then extends this method to a more difficult …

Deep Reinforcement Learning (DRL) has been applied successfully to many robotic
applications. However, the large number of trials needed for training is a key issue. Most of …

Deep reinforcement learning (RL) has emerged as a promising approach for autonomously
acquiring complex behaviors from low-level sensor observations. Although a large portion of …

Data efficiency in robotic skill acquisition is crucial for operating robots in varied small-batch
assembly settings. To operate in such environments, robots must have robust obstacle …

Recent advances in deep reinforcement learning (RL) have demonstrated its potential to
learn complex robotic manipulation tasks. However, RL still requires the robot to collect a …

The DARPA subterranean challenge requires teams of robots to traverse difficult and
diverse underground environments. Traversing small gaps is one of the challenging …

Reinforcement Learning (RL) is a machine learning framework for artificially intelligent
systems to solve a variety of complex problems. Recent years has seen a surge of …