149 research outputs found
Emergence of Human-comparable Balancing Behaviors by Deep Reinforcement Learning
This paper presents a hierarchical framework based on deep reinforcement
learning that learns a diversity of policies for humanoid balance control.
Conventional zero moment point based controllers perform limited actions during
under-actuation, whereas the proposed framework can perform human-like
balancing behaviors such as active push-off of ankles. The learning is done
through the design of an explainable reward based on physical constraints. The
simulated results are presented and analyzed. The successful emergence of
human-like behaviors through deep reinforcement learning proves the feasibility
of using an AI-based approach for learning humanoid balancing control in a
unified framework
Learning dynamic motor skills for terrestrial locomotion
The use of Deep Reinforcement Learning (DRL) has received significantly increased attention
from researchers within the robotics field following the success of AlphaGo, which demonstrated
the superhuman capabilities of deep reinforcement algorithms in terms of solving complex
tasks by beating professional GO players. Since then, an increasing number of researchers
have investigated the potential of using DRL to solve complex high-dimensional robotic tasks,
such as legged locomotion, arm manipulation, and grasping, which are difficult tasks to solve
using conventional optimization approaches.
Understanding and recreating various modes of terrestrial locomotion has been of long-standing interest to roboticists. A large variety of applications, such as rescue missions,
disaster responses and science expeditions, strongly demand mobility and versatility in legged
locomotion to enable task completion. In order to create useful physical robots, it is necessary
to design controllers to synthesize the complex locomotion behaviours observed in humans
and other animals.
In the past, legged locomotion was mainly achieved via analytical engineering approaches.
However, conventional analytical approaches have their limitations, as they require relatively
large amounts of human effort and knowledge. Machine learning approaches, such as DRL,
require less human effort compared to analytical approaches. The project conducted for this
thesis explores the feasibility of using DRL to acquire control policies comparable to, or better
than, those acquired through analytical approaches while requiring less human effort.
In this doctoral thesis, we developed a Multi-Expert Learning Architecture (MELA) that
uses DRL to learn multi-skill control policies capable of synthesizing a diverse set of dynamic
locomotion behaviours for legged robots. We first proposed a novel DRL framework for the
locomotion of humanoid robots. The proposed learning framework is capable of acquiring
robust and dynamic motor skills for humanoids, including balancing, walking, standing-up
fall recovery. We subsequently improved upon the learning framework and design a novel
multi-expert learning architecture that is capable of fusing multiple motor skills together in
a seamless fashion and ultimately deploy this framework on a real quadrupedal robot. The
successful deployment of learned control policies on a real quadrupedal robot demonstrates
the feasibility of using an Artificial Intelligence (AI) based approach for real robot motion control
Multi-expert learning of adaptive legged locomotion
Achieving versatile robot locomotion requires motor skills which can adapt to
previously unseen situations. We propose a Multi-Expert Learning Architecture
(MELA) that learns to generate adaptive skills from a group of representative
expert skills. During training, MELA is first initialised by a distinct set of
pre-trained experts, each in a separate deep neural network (DNN). Then by
learning the combination of these DNNs using a Gating Neural Network (GNN),
MELA can acquire more specialised experts and transitional skills across
various locomotion modes. During runtime, MELA constantly blends multiple DNNs
and dynamically synthesises a new DNN to produce adaptive behaviours in
response to changing situations. This approach leverages the advantages of
trained expert skills and the fast online synthesis of adaptive policies to
generate responsive motor skills during the changing tasks. Using a unified
MELA framework, we demonstrated successful multi-skill locomotion on a real
quadruped robot that performed coherent trotting, steering, and fall recovery
autonomously, and showed the merit of multi-expert learning generating
behaviours which can adapt to unseen scenarios
Unsupervised Deep Cross-Language Entity Alignment
Cross-lingual entity alignment is the task of finding the same semantic
entities from different language knowledge graphs. In this paper, we propose a
simple and novel unsupervised method for cross-language entity alignment. We
utilize the deep learning multi-language encoder combined with a machine
translator to encode knowledge graph text, which reduces the reliance on label
data. Unlike traditional methods that only emphasize global or local alignment,
our method simultaneously considers both alignment strategies. We first view
the alignment task as a bipartite matching problem and then adopt the
re-exchanging idea to accomplish alignment. Compared with the traditional
bipartite matching algorithm that only gives one optimal solution, our
algorithm generates ranked matching results which enabled many potentials
downstream tasks. Additionally, our method can adapt two different types of
optimization (minimal and maximal) in the bipartite matching process, which
provides more flexibility. Our evaluation shows, we each scored 0.966, 0.990,
and 0.996 Hits@1 rates on the DBP15K dataset in Chinese, Japanese, and French
to English alignment tasks. We outperformed the state-of-the-art method in
unsupervised and semi-supervised categories. Compared with the state-of-the-art
supervised method, our method outperforms 2.6% and 0.4% in Ja-En and Fr-En
alignment tasks while marginally lower by 0.2% in the Zh-En alignment task.Comment: 17 pages,5 figures, Accepted by ECML PKDD 2023(Research Track
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