Soft Pneumatic Actuators (SPAs) have recently become popular for use as fingers in robotic hands because of their inherent compliance, low cost, and ease of construction. We seek to overcome two key limitations which limit SPAs' abilities to grasp and manipulate objects: 1) Current SPAs lack position or force sensor feedback, which prevents controlling them precisely (e.g. to achieve a hand preshape or apply a specified pushing force), and 2) the tip of the SPA is compliant and has high friction against common surfaces, causing the SPA to stick against surfaces when grasping objects from above. To overcome the first limitation we propose methods to integrate soft eGaIn sensors into SPAs and controllers that use these sensors' feedback for position and force control. To overcome the second limitation, we explore embedding rigid fingernails into the tip of the SPA so that the finger does not stick against surfaces and can wedge under objects. Our experiments suggest that we can achieve low steady-state error and overshoot in position and force using feed-forward models that relate pressure, force, and curvature along with a PID controller. We also compare several fingernail designs and show that the best-performing design significantly outperforms having no fingernails when grasping a set of common objects from a table.

Evolutionary transparent cellular neural networks (ETCNNs) are designed and developed based on non-uniform cellular automata (CA). The phylogenetic level of the ETCNNs shows fractal numbers of synaptic layers in their topological morphology, and an extended backpropagation (BP) learning algorithm is induced through a transparent signal propagation mechanism at the epigenetic level. Therefore, an ETCNN deserves to be considered as a new paradigm of complex adaptive systems in artificial life. In simulations, ETCNNs have been applied to various function approximation problems with successful results for a relatively large number of training sets, and have shown convergent behaviors, both in the neural network structure and in the synaptic weights.