In the domain of facial skin treatment, personalization of treatment methods and management of treatment effectiveness among users/patients become the key challenges. Our approach to addressing the challenges is to develop a treatment recommender by utilizing a policy of reinforcement learning and continuously optimizing the policy to learn the variability. We leverage algorithmic decision-making through the Reinforcement Learning (RL) model's policy to personalize recommendations according to the unique characteristics and treatment responses of each user. This RL-based recommender should provide a high performance of personalizing treatment recommendations and continue to learn user-specific effectiveness of treatment methods. Preliminary results demonstrate the system's capacity to provide targeted, effective skin care recommendations, significantly enhancing user satisfaction and adherence to treatments.

Establishing dense correspondences across semantically similar images is one of the challenging tasks due to the significant intra-class variations and background clutters. To solve these problems, numerous methods have been proposed, focused on learning feature extractor or cost aggregation independently, which yields sub-optimal performance. In this paper, we propose a novel framework for jointly learning feature extraction and cost aggregation for semantic correspondence. By exploiting the pseudo labels from each module, the networks consisting of feature extraction and cost aggregation modules are simultaneously learned in a boosting fashion. Moreover, to ignore unreliable pseudo labels, we present a confidence-aware contrastive loss function for learning the networks in a weakly-supervised manner. We demonstrate our competitive results on standard benchmarks for semantic correspondence.

We propose a novel framework for 3D human recovery from a single image that integrates meta-learning with test-time optimization. Compared to previous optimization-based or learning-based methods that showed limited generalization ability, the test-time optimization framework enables us to estimate an optimal network parameter, which yields better performance and generalization ability, but it is highly sensitive to an initial network parameter. To alleviate this, we present a meta-learning framework to learn better initial parameters for test-time optimization. Experimental results on standard benchmarks show that the proposed framework boost the test-time optimization performance compared to state-of-the-arts.

Popcorn kernels are a natural, edible, and inexpensive material that has the potential to rapidly expand with high force upon application of heat. Although this transition is irreversible, it carries potential for several robotic applications. Here, we examine relevant characteristics of three types of kernels including expansion ratio, transition temperature, popping force, compression strength, and biodegradability. We test the viability of popping by hot oil, hot air, microwaves, and direct contact with heated Nichrome wire. As kernels can change from regular to (larger) irregular shapes, we examine the change in inter-granular friction and propose their use as granular fluids in jamming actuators, without the need for a vacuum pump. Furthermore, as a proof-of-concept, we also demonstrate the use of popcorn-driven actuation in soft, compliant, and rigid-link grippers. Serving as a first introduction of popcorn into robotics, we hope this paper will inspire novel mechanisms for multi-functional designs.

Recently, dynamic access control models are proposed to restrict access domain appropriately in multi-layered defense. However, policy description languages proposed so far can not express the models effectively in proper granularity. In this paper, we propose a policy description language which can designate precise condition for access control by using dynamic status of application process. Using the proposed language, we compose the policy of SELinux which is major implementation achieving multi-layered defense and confirm the advantages of the proposed language by evaluating the response and the expressiveness.