Redundant tendon-driven systems such as the human hand or the ACT robotic hand are high-dimensional and nonlinear systems that make traditional control strategies ineffective. The synergy hypothesis from neuroscience suggests that employing dimensionality reduction techniques can simplify the system without a major loss in function. We define a dimensionality reduction framework consisting of separate observation and activation synergies, a first-order model, and an optimal controller. The framework is implemented for two example tasks: adaptive control of thumb posture and hybrid position/force control to enable dynamic handwriting.

We apply path integral reinforcement learning to a biomechanically accurate dynamics model of the index finger and then to the Anatomically Correct Testbed (ACT) robotic hand. We illustrate the applicability of Policy Improvement with Path Integrals (PI2) to parameterized and non-parameterized control policies. This method is based on sampling variations in control, executing them in the real world, and minimizing a cost function on the resulting performance. Iteratively improving the control policy based on real-world performance requires no direct modeling of tendon network nonlinearities and contact transitions, allowing improved task performance.

Muscle synergies are hypothesized as a way to reduce the control space of redundant biological neuromuscular control. Robotics researchers are starting to use grasping synergies to simplify the task of coordinating many joints for complex manipulators. This is especially useful for hand movement control as there are many joints and muscles to control to achieve even simple tasks. This paper uses the Anatomically Correct Testbed (ACT) Robotic Hand to understand how task-specific synergies may be formed and used for complex robotic manipulation tasks such as writing. A comparison is made between synergies formed from (1) general hand movements, (2) task-specific demonstration of generic writing, and (3) practice of writing a specific letter. Results showed that performance using task specific demonstration synergies outperforms performance using general-purpose synergies in terms of completion time, energy expenditure, and trajectory error. Performance using practiced synergy outperforms the performance using demonstration synergies in trajectory error, even though there was no statistical difference in completion time and energy expenditure. These results indicate that task specific synergies from demonstration and practice allow a robotic hand to write better than using more generic synergies that may work for other tasks. How general/specific these synergies should be to optimize the performance of different complex tasks without learning too many specific synergies is an interesting topic for the future.