Tesi etd-09212018-160240
Link copiato negli appunti
Tipo di tesi
Perfezionamento
Autore
GEORGE THURUTHEL, THOMAS
Indirizzo email
thomasgeorge2571766@gmail.com
URN
etd-09212018-160240
Titolo
Machine learning approaches for soft robot control
Settore scientifico disciplinare
ING-IND/34
Corso di studi
INGEGNERIA - Biorobotics
Commissione
relatore CIANCHETTI, MATTEO
Parole chiave
- Control
- Dynamics
- Embodied Intelligence
- Kinematics
- Machine Learning
- Soft Robotics
- State Estimation
Data inizio appello
11/02/2019;
Disponibilità
completa
Riassunto analitico
This thesis presents the application of various machine learning techniques for control
of soft robots. Simulation and experimental studies are described that show
the feasibility of kinematic and dynamic controllers developed using learning techniques.
The approaches are validated for both open loop and closed loop task space
control. Subsequently, the role of morphology and its effect on control strategies
are analyzed for two different cases; First, on a simulated octopus model and then
experimentally on a soft manipulator for self stabilizing dynamic behavior. Finally,
a short foray into embedded sensing is presented to eventually strive towards self
sufficient embodied systems
For the static case, global inverse kinematic solutions are directly learned, enabling
us to develop computationally cheap controllers. The redundancy in the actuation
system and hysteresis effects are the main factors to be considered while
learning the static model. Using a learned network, equivalent in form to the traditional
resolved motion rate controller, we develop accurate and easy-to-develop
static controllers. Yet, this kind of controllers is energy inefficient and perform slow
motions in order to maintain the statics assumption.
Natural and fast motions can be derived using dynamic controllers. The problem
is on obtaining the mapping from actuator forces to the time evolution of system
states. A recurrent neural network was used to learn the forward dynamic model.
Although the fundamental model is more intricate, the sampling and training time
to obtain the model is still faster than the static case. With the new forward dynamic
model, any numerical optimization method can be adopted to generate the control
inputs. Consideration of the manipulator dynamics brings about fascinating motion
behaviors. For instance, we were able to determine open loop trajectories that are
globally stable and able to reach workspace regions that were not reachable statically.
Later, we use a recent technique called model-based reinforcement learning
for obtaining global closed loop control policies. These controllers were found ideal
for controlling the soft manipulator dynamically when an unknown load is added,
Finally, we perform behavioral studies on the reaching behavior of the biological
Octopus using the same control approach and a simulated soft manipulator, which
is morphologically similar to the animal. This provided us interesting insights into
the role of morphology in shaping behavior. A short detour into modelling of soft
resistive sensors is then presented. For this work, we adopt an approach similar
to the human perceptive system for modelling embedded sensors. We demonstrate
multi-modal sensing with randomly embedded strain sensors; all of the same kind.
of soft robots. Simulation and experimental studies are described that show
the feasibility of kinematic and dynamic controllers developed using learning techniques.
The approaches are validated for both open loop and closed loop task space
control. Subsequently, the role of morphology and its effect on control strategies
are analyzed for two different cases; First, on a simulated octopus model and then
experimentally on a soft manipulator for self stabilizing dynamic behavior. Finally,
a short foray into embedded sensing is presented to eventually strive towards self
sufficient embodied systems
For the static case, global inverse kinematic solutions are directly learned, enabling
us to develop computationally cheap controllers. The redundancy in the actuation
system and hysteresis effects are the main factors to be considered while
learning the static model. Using a learned network, equivalent in form to the traditional
resolved motion rate controller, we develop accurate and easy-to-develop
static controllers. Yet, this kind of controllers is energy inefficient and perform slow
motions in order to maintain the statics assumption.
Natural and fast motions can be derived using dynamic controllers. The problem
is on obtaining the mapping from actuator forces to the time evolution of system
states. A recurrent neural network was used to learn the forward dynamic model.
Although the fundamental model is more intricate, the sampling and training time
to obtain the model is still faster than the static case. With the new forward dynamic
model, any numerical optimization method can be adopted to generate the control
inputs. Consideration of the manipulator dynamics brings about fascinating motion
behaviors. For instance, we were able to determine open loop trajectories that are
globally stable and able to reach workspace regions that were not reachable statically.
Later, we use a recent technique called model-based reinforcement learning
for obtaining global closed loop control policies. These controllers were found ideal
for controlling the soft manipulator dynamically when an unknown load is added,
Finally, we perform behavioral studies on the reaching behavior of the biological
Octopus using the same control approach and a simulated soft manipulator, which
is morphologically similar to the animal. This provided us interesting insights into
the role of morphology in shaping behavior. A short detour into modelling of soft
resistive sensors is then presented. For this work, we adopt an approach similar
to the human perceptive system for modelling embedded sensors. We demonstrate
multi-modal sensing with randomly embedded strain sensors; all of the same kind.
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