We propose a novel constraint-handling technique for the covariance matrix adaptation evolution strategy (CMA-ES). The proposed technique is aimed at solving explicitly constrained black-box continuous optimization problems, in which the explicit constraint is a constraint whereby the computational time for the constraint violation and its (numerical) gradient are negligible compared to that for the objective function. This method is designed to realize two invariance properties: invariance to the affine transformation of the search space, and invariance to the increasing transformation of the objective and constraint functions. The CMA-ES is designed to possess these properties for handling difficulties that appear in black-box optimization problems, such as non-separability, ill-conditioning, ruggedness, and the different orders of magnitude in the objective. The proposed constraint-handling technique (CHT), known as ARCH, modifies the underlying CMA-ES only in terms of the ranking of the candidate solutions. It employs a repair operator and an adaptive ranking aggregation strategy to compute the ranking. We developed test problems to evaluate the effects of the invariance properties, and performed experiments to empirically verify the invariance of the algorithm. We compared the proposed method with other CHTs on the CEC 2006 constrained optimization benchmark suite to demonstrate its efficacy. Empirical studies reveal that ARCH is able to exploit the explicitness of the constraint functions effectively, sometimes even more efficiently than an existing box-constraint handling technique on box-constrained problems, while exhibiting the invariance properties. Moreover, ARCH overwhelmingly outperforms CHTs by not exploiting the explicit constraints in terms of the number of objective function calls.

In this study, we consider black-box minimization problems with non-convex constraints, where the constraints are significantly cheaper to evaluate than the objective. Non-convex constraints generally make it difficult to solve problems using evolutionary approaches. In this paper, we revisit a conventional technique called decoder constraint handling, which transforms a feasible non-convex domain into an easy-to-control convex set. This approach is promising because it transforms a constrained problem into an almost unconstrained one. However, its application has been considerably limited, because designing or training such a nonlinear decoder requires domain knowledge or manually prepared training data. To fully automate the decoder design, we use deep generative models. We propose a novel scheme to train a deep generative model without using manually prepared training data. For this purpose, we first train feasible solution samplers, which are deep neural networks, using the constraint functions. Subsequently, we train another deep generative model using the data generated from the trained samplers as the training data. The proposed framework is applied to tasks inspired by topology optimization problems. The empirical study demonstrates that the proposed approach can locate better solutions with fewer objective function evaluations than the existing approach.

A novel explicit constraint handling technique for the covariance matrix adaptation evolution strategy (CMA-ES) is proposed. The proposed constraint handling exhibits two invariance properties. One is the invariance to arbitrary element-wise increasing transformation of the objective and constraint functions. The other is the invariance to arbitrary affine transformation of the search space. The proposed technique virtually transforms a constrained optimization problem into an unconstrained optimization problem by considering an adaptive weighted sum of the ranking of the objective function values and the ranking of the constraint violations that are measured by the Mahalanobis distance between each candidate solution to its projection onto the boundary of the constraints. Simulation results are presented and show that the CMA-ES with the proposed constraint handling exhibits the affine invariance and performs similarly to the CMA-ES on unconstrained counterparts.

This paper proposes a piercing based grasping by using the self-tightening effect of objects with elasticity. We suppose a piercing hand with palm where the piercing motion by needles can be independently achieved irrespective of the palm motion. The palm first approaches and touches with the object, with a slight pushing motion. This motion produces an increase of potential energy of object. After the motion, the needles pierce the object. When the object is lifted up, the accumulated potential energy is released and the object tries to recover the original shape under the piercing condition. We found a particular mechanical configuration between the object and the needle, under which the constraint of object is tightened due to the object deformation during the release of the potential energy. In order to confirm the robustness of the proposed method, we have done a couple of experiments. The results show that the proposed method keeps an extremely high robustness compared with the other piercing methods.

In this paper, we employ Fung’s viscoelastic model discussed by Tiezzi and Kao to study the experimental data presented by Sakamoto et al. for grasping viscoelastic objects using a parallel-jaw gripper. The viscoelastic contact modeling presented in this paper is characterized by two separate responses: elastic response and temporal response. Two main and intriguing results were found in the modeling and analysis of experimental data. The first is the consistency on the normalized coefficients for the curve fitting of the temporal response during the relaxation period of the grasping. Such consistency suggests that the proposed model is applicable to the grasping task at hand. The other result is the generic pattern of the elastic response deduced from the experimental data. The pattern of elastic response represents different physical significance of grasping which involves viscoelastic contact interface.

This paper discusses an optimum design approach for robotic hands by considering the characteristics of visco-elasticity of food. "Norimaki-sushi" is taken as an example for food. We first show that the dynamic characteristics of such food can be expressed by utilizing the Maxwell model with two layers. Based on dynamic parameters obtained by experiments, we show the relationship among the total working time, the plastic deformation of food after the grasping motion, the hand stiffness, and the operating velocity of the hand. We newly found an interesting behavior of food that allows to find an optimum set of the design parameters for achieving the minimum plastic deformation of food.