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7.1 Tendon driven versus torque driven actuation
The gap in the functionality and robustness between robotic and human hands has its
origins in our lack of understanding of design principles based on control theoretic ideas
applicable to complex biomechanical structures such as the hand. From the control
theoretic standpoint, the control of a highly dimensional and nonlinear stochastic plant
of the complexity of a robotic or biomechanical hand is not an easy task–which also makes
it difficult to understand the neuromuscular control of the hand. To appreciate the high
dimensionality, it is enough to consider that more than 35 tendons must be controlled by
the nervous system (Freivalds 2000). Some critical questions that remain open are:
• What strategies does the nervous system use for moving the finger given the geomet-rical
and mechanical characteristics of the muscular-tendon-bone structure? How
sensitive these strategies are with respect to variations in the underlying dynamics
and moment arm geometry?
There are few important differences between torque driven and tendons driven bio-mechanical
structures. In particular, in tendon driven systems, the number of control
variables is usually higher than the number of corresponding controls in torque driven
systems. For example, for the case of the index finger, there are 7 actuating tendons which
produce the required torque around the 3 joins, while in torque actuated mechanical fin-gers
systems, 3 torque based control variables are sufficient to produce planar movements.
An additional component is that, the tendon actuation is constrained since tendons can
only pull and not push while in most robotic systems that are torque driven, the control
variables can take negative and positive values to generate negative or positive torques
226
Object Description
| Title | Iterative path integral stochastic optimal control: theory and applications to motor control |
| Author | Theodorou, Evangelos A. |
| Author email | etheodor@usc.edu; theo0027@umn.edu |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Computer Science |
| School | Viterbi School of Engineering |
| Date defended/completed | 2011-01-11 |
| Date submitted | 2011 |
| Restricted until | Unrestricted |
| Date published | 2011-04-29 |
| Advisor (committee chair) | Schaal, Stefan |
| Advisor (committee member) |
Valero-Cuevas, Francisco Sukhatme, Gaurav S. Todorov, Emo Schweighofer, Nicolas |
| Abstract | Motivated by the limitations of current optimal control and reinforcement learning methods in terms of their efficiency and scalability, this thesis proposes an iterative stochastic optimal control approach based on the generalized path integral formalism. More precisely, we suggest the use of the framework of stochastic optimal control with path integrals to derive a novel approach to RL with parameterized policies. While solidly grounded in value function estimation and optimal control based on the stochastic Hamilton Jacobi Bellman (HJB) equation, policy improvements can be transformed into an approximation problem of a path integral which has no open algorithmic parameters other than the exploration noise. The resulting algorithm can be conceived of as model-based, semi-model-based, or even model free, depending on how the learning problem is structured. The new algorithm, Policy Improvement with Path Integrals (PI2), demonstrates interesting similarities with previous RL research in the framework of probability matching and provides intuition why the slightly heuristically motivated probability matching approach can actually perform well. Applications to high dimensional robotic systems are presented for a variety of tasks that require optimal planning and gain scheduling.; In addition to the work on generalized path integral stochastic optimal control, in this thesis we extend model based iterative optimal control algorithms to the stochastic setting. More precisely we derive the Differential Dynamic Programming algorithm for stochastic systems with state and control multiplicative noise. Finally, in the last part of this thesis, model based iterative optimal control methods are applied to bio-mechanical models of the index finger with the goal to find the underlying tendon forces applied for the movements of, tapping and flexing. |
| Keyword | stochastic optimal control; reinforcement learning,; robotics |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m3804 |
| Contributing entity | University of Southern California |
| Rights | Theodorou, Evangelos A. |
| Repository name | Libraries, University of Southern California |
| Repository address | Los Angeles, California |
| Repository email | cisadmin@lib.usc.edu |
| Filename | etd-Theodorou-4581 |
| Archival file | uscthesesreloadpub_Volume14/etd-Theodorou-4581.pdf |
Description
| Title | Page 240 |
| Contributing entity | University of Southern California |
| Repository email | cisadmin@lib.usc.edu |
| Full text | 7.1 Tendon driven versus torque driven actuation The gap in the functionality and robustness between robotic and human hands has its origins in our lack of understanding of design principles based on control theoretic ideas applicable to complex biomechanical structures such as the hand. From the control theoretic standpoint, the control of a highly dimensional and nonlinear stochastic plant of the complexity of a robotic or biomechanical hand is not an easy task–which also makes it difficult to understand the neuromuscular control of the hand. To appreciate the high dimensionality, it is enough to consider that more than 35 tendons must be controlled by the nervous system (Freivalds 2000). Some critical questions that remain open are: • What strategies does the nervous system use for moving the finger given the geomet-rical and mechanical characteristics of the muscular-tendon-bone structure? How sensitive these strategies are with respect to variations in the underlying dynamics and moment arm geometry? There are few important differences between torque driven and tendons driven bio-mechanical structures. In particular, in tendon driven systems, the number of control variables is usually higher than the number of corresponding controls in torque driven systems. For example, for the case of the index finger, there are 7 actuating tendons which produce the required torque around the 3 joins, while in torque actuated mechanical fin-gers systems, 3 torque based control variables are sufficient to produce planar movements. An additional component is that, the tendon actuation is constrained since tendons can only pull and not push while in most robotic systems that are torque driven, the control variables can take negative and positive values to generate negative or positive torques 226 |
