125 research outputs found
Synthesising Novel Movements through Latent Space Modulation of Scalable Control Policies
We propose a novel methodology for learning and synthesising whole
classes of high dimensional movements from a limited set of demonstrated examples
that satisfy some underlying ’latent’ low dimensional task constraints. We
employ non-linear dimensionality reduction to extract a canonical latent space
that captures some of the essential topology of the unobserved task space. In
this latent space, we identify suitable parametrisation of movements with control
policies such that they are easily modulated to generate novel movements from
the same class and are robust to perturbations. We evaluate our method on controlled
simulation experiments with simple robots (reaching and periodic movement
tasks) as well as on a data set of very high-dimensional human (punching)
movements.We verify that we can generate a continuum of new movements from
the demonstrated class from only a few examples in both robotic and human data
Latent Spaces for Dynamic Movement Primitives
Dynamic movement primitives (DMPs) have been
proposed as a powerful, robust and adaptive tool for planning
robot trajectories based on demonstrated example movements.
Adaptation of DMPs to new task requirements becomes difficult
when demonstrated trajectories are only available in joint
space, because their parameters do not in general correspond
to variables meaningful for the task. This problem becomes
more severe with increasing number of degrees of freedom and
hence is particularly an issue for humanoid movements. It has
been shown that DMP parameters can directly relate to task
variables, when DMPs are learned in latent spaces resulting
from dimensionality reduction of demonstrated trajectories.
As we show here, however, standard dimensionality reduction
techniques do not in general provide adequate latent spaces
which need to be highly regular.
In this work we concentrate on learning discrete (point-topoint)
movements and propose a modification of a powerful
nonlinear dimensionality reduction technique (Gaussian Process
Latent Variable Model). Our modification makes the GPLVM
more suitable for the use of DMPs by favouring latent spaces
with highly regular structure. Even though in this case the
user has to provide a structure hypothesis we show that its
precise choice is not important in order to achieve good results.
Additionally, we can overcome one of the main disadvantages
of the GPLVM with this modification: its dependence on the
initialisation of the latent space. We motivate our approach on
data from a 7-DoF robotic arm and demonstrate its feasibility
on a high-dimensional human motion capture data set
Nonlinear Dimensionality Reduction for Motion Synthesis and Control
Synthesising motion of human character animations or humanoid robots is vastly complicated
by the large number of degrees of freedom in their kinematics. Control spaces
become so large, that automated methods designed to adaptively generate movements
become computationally infeasible or fail to find acceptable solutions.
In this thesis we investigate how demonstrations of previously successful movements
can be used to inform the production of new movements that are adapted to
new situations. In particular, we evaluate the use of nonlinear dimensionality reduction
techniques to find compact representations of demonstrations, and investigate how
these can simplify the synthesis of new movements.
Our focus lies on the Gaussian Process Latent Variable Model (GPLVM), because it
has proven to capture the nonlinearities present in the kinematics of robots and humans.
We present an in-depth analysis of the underlying theory which results in an alternative
approach to initialise the GPLVM based on Multidimensional Scaling. We show that
the new initialisation is better suited than PCA for nonlinear, synthetic data, but have
to note that its advantage shrinks on motion data.
Subsequently we show that the incorporation of additional structure constraints
leads to low-dimensional representations which are sufficiently regular so that once
learned dynamic movement primitives can be adapted to new situations without need
for relearning. Finally, we demonstrate in a number of experiments where movements
are generated for bimanual reaching, that, through the use of nonlinear dimensionality
reduction, reinforcement learning can be scaled up to optimise humanoid movements
WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM
Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments
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
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Learning Parameterized Skills
One of the defining characteristics of human intelligence is the ability to acquire and refine skills. Skills are behaviors for solving problems that an agent encounters often—sometimes in different contexts and situations—throughout its lifetime. Identifying important problems that recur and retaining their solutions as skills allows agents to more rapidly solve novel problems by adjusting and combining their existing skills.
In this thesis we introduce a general framework for learning reusable parameterized skills. Reusable skills are parameterized procedures that—given a description of a problem to be solved—produce appropriate behaviors or policies. They can be sequentially and hierarchically combined with other skills to produce progressively more abstract and temporally extended behaviors.
We identify three major challenges involved in the construction of such skills. First, an agent should be capable of solving a small number of problems and generalizing these experiences to construct a single reusable skill. The skill should be capable of producing appropriate behaviors even when applied to yet unseen variations of a problem. We introduce a method for estimating properties of the lower-dimensional manifold on which problem solutions lie. This allows for the construction of unified models for predicting policies from task parameters.
Secondly, the agent should be able to identify when a skill can be hierarchically decomposed into specialized sub-skills. We observe that the policy manifold may be composed of disjoint, piecewise-smooth charts, each one encoding solutions for a subclass of problems. Identifying and modeling sub-skills allows for the aggregation of related behaviors into single, more abstract skills.
Finally, the agent should be able to actively select on which problems to practice in order to more rapidly become competent in a skill. Thoughtful and deliberate practice is one of the defining characteristics of human expert performance. By carefully choosing on which problems to practice the agent might more rapidly construct a skill that performs well over a wide range of problems.
We address these challenges via a general framework for skill acquisition. We evaluate it on simulated decision-problems and on a physical humanoid robot, and demonstrate that it allows for the efficient and active construction of reusable skills
White Paper on Digital and Complex Information
Information is one of the main traits of the contemporary era. Indeed there aremany perspectives to define the present times, such as the Digital Age, the Big Dataera, the Fourth Industrial Revolution, the fourth Paradigm of science, and in all ofthem information, gathered, stored, processed and transmitted, plays a key role.Technological developments in the last decades such as powerful computers, cheaperand miniaturized solutions as smartphones, massive optical communication, or theInternet, to name few, have enabled this shift to the Information age. This shift hasdriven daily life, cultural and social deep changes, in work and personal activities,on access to knowledge, information spreading, altering interpersonal relations orthe way we interact in public and private sphere, in economy and politics, pavingthe way to globalizationPeer reviewe
The organisation of excess : movement, analysis and alter-globalisation
This thesis involves a reading of the political problematics of the alter- globalisation movement. The thesis suggests that some of the fundamental problems faced by the alter-globalisation movement can be traced to its emergence in the crucible of intensive moments of political activity, at, for example, anti-summit protests. The expansion of political possibilities experienced during such moments, stands in contrast to the constricted sense of political possibility experienced during more quotidian times. To analyse the relationship between the two sets of political experiences we examine Deleuze and Guattari’s concepts of antiproduction, the Body without Organs, and the socius; which we will argue carries the inheritance of Bataille’s concept of nonproductive expenditure. In the light of this theory we conceptualise intensive moments of political activity as moments of excess. We then examine the concept of an analytical war machine as a mode of organisation that can operate across the ruptures and discontinuities produced by moments of excess. The aim is, in part, to provide a mode of analysis that can operate across periods of transformation, even when the very presuppositions of the analysis are themselves subject to change. To do so we develop the concept of an analytical territory and examine the practices of the alter-globalisation movement through Deleuze and Guattari’s territorial concept of the refrain. This thesis, then, provides a novel and innovative reading of the political problematics of the alter-globalisation movement, and fundamentally reconceptualises some familiar repertoires and practices. At the same time, however, the thesis can be read as a novel and innovative interpretation of the political problematics contained in Deleuze and Guattari’s work
Structured manifolds for motion production and segmentation : a structured Kernel Regression approach
Steffen JF. Structured manifolds for motion production and segmentation : a structured Kernel Regression approach. Bielefeld (Germany): Bielefeld University; 2010
Motion planning and reactive control on learnt skill manifolds
We propose a novel framework for motion planning and control that is based on a
manifold encoding of the desired solution set. We present an alternate, model-free,
approach to path planning, replanning and control. Our approach is founded on the
idea of encoding the set of possible trajectories as a skill manifold, which can be learnt
from data such as from demonstration.
We describe the manifold representation of skills, a technique for learning from
data and a method for generating trajectories as geodesics on such manifolds. We
extend the trajectory generation method to handle dynamic obstacles and constraints.
We show how a state metric naturally arises from the manifold encoding and how this
can be used for reactive control in an on-line manner.
Our framework tightly integrates learning, planning and control in a computationally
efficient representation, suitable for realistic humanoid robotic tasks that are defined
by skill specifications involving high-dimensional nonlinear dynamics, kinodynamic
constraints and non-trivial cost functions, in an optimal control setting. Although,
in principle, such problems can be handled by well understood analytical
methods, it is often difficult and expensive to formulate models that enable the analytical
approach.
We test our framework with various types of robotic systems – ranging from a
3-link arm to a small humanoid robot – and show that the manifold encoding gives
significant improvements in performance without loss of accuracy. Furthermore, we
evaluate the framework against a state-of-the-art imitation learning method. We show
that our approach, by learning manifolds of robotic skills, allows for efficient planning
and replanning in changing environments, and for robust and online reactive control
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