Robot learning from randomized simulations: A review
The rise of deep learning has caused a paradigm shift in robotics research, favoring
methods that require large amounts of data. Unfortunately, it is prohibitively expensive to …
methods that require large amounts of data. Unfortunately, it is prohibitively expensive to …
A survey on policy search algorithms for learning robot controllers in a handful of trials
K Chatzilygeroudis, V Vassiliades… - IEEE Transactions …, 2019 - ieeexplore.ieee.org
Most policy search (PS) algorithms require thousands of training episodes to find an
effective policy, which is often infeasible with a physical robot. This survey article focuses on …
effective policy, which is often infeasible with a physical robot. This survey article focuses on …
Go fetch!-dynamic grasps using boston dynamics spot with external robotic arm
S Zimmermann, R Poranne… - 2021 IEEE International …, 2021 - ieeexplore.ieee.org
We combine Boston Dynamics Spot® with a light-weight, external robot arm to perform
dynamic grasping maneuvers. While Spot is a reliable, robust and easy-to-control mobile …
dynamic grasping maneuvers. While Spot is a reliable, robust and easy-to-control mobile …
Evolving embodied intelligence from materials to machines
Natural lifeforms specialize to their environmental niches across many levels, from low-level
features such as DNA and proteins, through to higher-level artefacts including eyes, limbs …
features such as DNA and proteins, through to higher-level artefacts including eyes, limbs …
[HTML][HTML] Reset-free trial-and-error learning for robot damage recovery
The high probability of hardware failures prevents many advanced robots (eg, legged
robots) from being confidently deployed in real-world situations (eg, post-disaster rescue) …
robots) from being confidently deployed in real-world situations (eg, post-disaster rescue) …
Policy search in continuous action domains: an overview
Continuous action policy search is currently the focus of intensive research, driven both by
the recent success of deep reinforcement learning algorithms and the emergence of …
the recent success of deep reinforcement learning algorithms and the emergence of …
PIPPS: Flexible model-based policy search robust to the curse of chaos
Previously, the exploding gradient problem has been explained to be central in deep
learning and model-based reinforcement learning, because it causes numerical issues and …
learning and model-based reinforcement learning, because it causes numerical issues and …
Skill-based multi-objective reinforcement learning of industrial robot tasks with planning and knowledge integration
In modern industrial settings with small batch sizes it should be easy to set up a robot system
for a new task. Strategies exist, eg the use of skills, but when it comes to handling forces and …
for a new task. Strategies exist, eg the use of skills, but when it comes to handling forces and …
Fast online adaptation in robotics through meta-learning embeddings of simulated priors
Meta-learning algorithms can accelerate the model-based reinforcement learning (MBRL)
algorithms by finding an initial set of parameters for the dynamical model such that the …
algorithms by finding an initial set of parameters for the dynamical model such that the …
A survey of sim-to-real transfer techniques applied to reinforcement learning for bioinspired robots
The state-of-the-art reinforcement learning (RL) techniques have made innumerable
advancements in robot control, especially in combination with deep neural networks …
advancements in robot control, especially in combination with deep neural networks …