17 research outputs found
Bayesian Optimization Using Domain Knowledge on the ATRIAS Biped
Controllers in robotics often consist of expert-designed heuristics, which
can be hard to tune in higher dimensions. It is typical to use simulation to
learn these parameters, but controllers learned in simulation often don't
transfer to hardware. This necessitates optimization directly on hardware.
However, collecting data on hardware can be expensive. This has led to a recent
interest in adapting data-efficient learning techniques to robotics. One
popular method is Bayesian Optimization (BO), a sample-efficient black-box
optimization scheme, but its performance typically degrades in higher
dimensions. We aim to overcome this problem by incorporating domain knowledge
to reduce dimensionality in a meaningful way, with a focus on bipedal
locomotion. In previous work, we proposed a transformation based on knowledge
of human walking that projected a 16-dimensional controller to a 1-dimensional
space. In simulation, this showed enhanced sample efficiency when optimizing
human-inspired neuromuscular walking controllers on a humanoid model. In this
paper, we present a generalized feature transform applicable to non-humanoid
robot morphologies and evaluate it on the ATRIAS bipedal robot -- in simulation
and on hardware. We present three different walking controllers; two are
evaluated on the real robot. Our results show that this feature transform
captures important aspects of walking and accelerates learning on hardware and
simulation, as compared to traditional BO.Comment: 8 pages, submitted to IEEE International Conference on Robotics and
Automation 201
Reducing animator keyframes
The aim of this doctoral thesis is to present a body of work aimed at reducing the
time spent by animators manually constructing keyframed animation. To this end we
present a number of state of the art machine learning techniques applied to the domain
of character animation.
Data-driven tools for the synthesis and production of character animation have a good
track record of success. In particular, they have been adopted thoroughly in the games
industry as they allow designers as well as animators to simply specify the high-level
descriptions of the animations to be created, and the rest is produced automatically.
Even so, these techniques have not been thoroughly adopted in the film industry in
the production of keyframe based animation [Planet, 2012]. Due to this, the cost of
producing high quality keyframed animation remains very high, and the time of professional
animators is increasingly precious.
We present our work in four main chapters. We first tackle the key problem in the
adoption of data-driven tools for key framed animation - a problem called the inversion
of the rig function. Secondly, we show the construction of a new tool for data-driven
character animation called the motion manifold - a representation of motion
constructed using deep learning that has a number of properties useful for animation
research. Thirdly, we show how the motion manifold can be extended as a general
tool for performing data-driven animation synthesis and editing. Finally, we show how
these techniques developed for keyframed animation can also be adapted to advance
the state of the art in the games industry
Relationship descriptors for interactive motion adaptation
In this thesis we present an interactive motion adaptation scheme for close
interactions between skeletal characters and mesh structures, such as navigating
restricted environments and manipulating tools.
We propose a new spatial-relationship based representation to encode
character-object interactions describing the kinematics of the body parts by the
weighted sum of vectors relative to descriptor points selectively sampled over the
scene. In contrast to previous discrete representations that either only handle
static spatial relationships, or require offline, costly optimization processes, our
continuous framework smoothly adapts the motion of a character to deformations
in the objects and character morphologies in real-time whilst preserving the
original context and style of the scene.
We demonstrate the strength of working in our relationship-descriptor
space in tackling the issue of motion editing under large environment
deformations by integrating procedural animation techniques such as
repositioning contacts in an interaction whilst preserving the context and style of
the original animation.
Furthermore we propose a method that can be used to adapt animations
from template objects to novel ones by solving for mappings between the two in
our relationship-descriptor space effectively transferring an entire motion from
one object to a new one of different geometry whilst ensuring continuity across
all frames of the animation, as opposed to mapping static poses only as is
traditionally achieved.
The experimental results show that our method can be used for a wide
range of applications, including motion retargeting for dynamically changing
scenes, multi-character interactions, and interactive character control and
deformation transfer for scenes that involve close interactions. We further
demonstrate a key use case in retargeting locomotion to uneven terrains and
curving paths convincingly for bipeds and quadrupeds.
Our framework is useful for artists who need to design animated scenes
interactively, and modern computer games that allow users to design their own
virtual characters, objects and environments, such that they can recycle existing
motion data for a large variety of different configurations without the need to
manually reconfigure motion from scratch or store expensive combinations of
animation in memory. Most importantly it’s achieved in real-time
Description of motor control using inverse models
Humans can perform complicated movements like writing or running without giving them much thought. The scientific understanding of principles guiding the generation of these movements is incomplete. How the nervous system ensures stability or compensates for injury and constraints – are among the unanswered questions today. Furthermore, only through movement can a human impose their will and interact with the world around them. Damage to a part of the motor control system can lower a person’s quality of life. Understanding how the central nervous system (CNS) forms control signals and executes them helps with the construction of devices and rehabilitation techniques. This allows the user, at least in part, to bypass the damaged area or replace its function, thereby improving their quality of life.
CNS forms motor commands, for example a locomotor velocity or another movement task. These commands are thought to be processed through an internal model of the body to produce patterns of motor unit activity. An example of one such network in the spinal cord is a central pattern generator (CPG) that controls the rhythmic activation of synergistic muscle groups for overground locomotion. The descending drive from the brainstem and sensory feedback pathways initiate and modify the activity of the CPG. The interactions between its inputs and internal dynamics are still under debate in experimental and modelling studies. Even more complex neuromechanical mechanisms are responsible for some non-periodic voluntary movements. Most of the complexity stems from internalization of the body musculoskeletal (MS) system, which is comprised of hundreds of joints and muscles wrapping around each other in a sophisticated manner. Understanding their control signals requires a deep understanding of their dynamics and principles, both of which remain open problems.
This dissertation is organized into three research chapters with a bottom-up investigation of motor control, plus an introduction and a discussion chapter. Each of the three research chapters are organized as stand-alone articles either published or in preparation for submission to peer-reviewed journals. Chapter two introduces a description of the MS kinematic variables of a human hand. In an effort to simulate human hand motor control, an algorithm was defined that approximated the moment arms and lengths of 33 musculotendon actuators spanning 18 degrees of freedom. The resulting model could be evaluated within 10 microseconds and required less than 100 KB of memory. The structure of the approximating functions embedded anatomical and functional features of the modelled muscles, providing a meaningful description of the system. The third chapter used the developments in musculotendon modelling to obtain muscle activity profiles controlling hand movements and postures. The agonist-antagonist coactivation mechanism was responsible for producing joint stability for most degrees of freedom, similar to experimental observations. Computed muscle excitations were used in an offline control of a myoelectric prosthesis for a single subject. To investigate the higher-order generation of control signals, the fourth chapter describes an analytical model of CPG. Its parameter space was investigated to produce forward locomotion when controlled with a desired speed. The model parameters were varied to produce asymmetric locomotion, and several control strategies were identified. Throughout the dissertation the balance between analytical, simulation, and phenomenological modelling for the description of simple and complex behavior is a recurrent theme of discussion
Mechatronic Systems
Mechatronics, the synergistic blend of mechanics, electronics, and computer science, has evolved over the past twenty five years, leading to a novel stage of engineering design. By integrating the best design practices with the most advanced technologies, mechatronics aims at realizing high-quality products, guaranteeing at the same time a substantial reduction of time and costs of manufacturing. Mechatronic systems are manifold and range from machine components, motion generators, and power producing machines to more complex devices, such as robotic systems and transportation vehicles. With its twenty chapters, which collect contributions from many researchers worldwide, this book provides an excellent survey of recent work in the field of mechatronics with applications in various fields, like robotics, medical and assistive technology, human-machine interaction, unmanned vehicles, manufacturing, and education. We would like to thank all the authors who have invested a great deal of time to write such interesting chapters, which we are sure will be valuable to the readers. Chapters 1 to 6 deal with applications of mechatronics for the development of robotic systems. Medical and assistive technologies and human-machine interaction systems are the topic of chapters 7 to 13.Chapters 14 and 15 concern mechatronic systems for autonomous vehicles. Chapters 16-19 deal with mechatronics in manufacturing contexts. Chapter 20 concludes the book, describing a method for the installation of mechatronics education in schools