Kenji Urai

Design and control of a ray-mimicking soft robot based on morphological features for adaptive deformation

Underwater tasks are diversified and articulated. The environment in which they must be accomplished is often unconstrained and unpredictable. Operating AUVs assuring safety of the robot and of its surrounding is therefore very difficult. On the other hand, many fishes are able to easily move in the same environments. A crucial factor for this capability is their body, which consists primarily of elastic and soft structures that enable both complex movement and adaptation to the environment. Among the most efficient swimmers we find rays, which show abilities like high speed turning and omnidirectional swimming. In this paper we propose an underwater soft robot based on the morphological features of rays. We mimic both their radially skeletal structure with independent actuators for each bone and the compliance of their fins. This flexibility of the structure provides an adaptive deformation that allows our robot to swim smoothly and safely.

Confidence-based roadmap using Gaussian process regression for a robot control

To achieve a realistic task by a recent complicated robot, a practical motion planning method is important. Especially in this decade, sampling-based motion planning methods have become popular thanks to recent high performance computers. In sampling-based motion planning, a graph that covers the state space is constructed based on reachability between node pairs, and the motion is planned using the graph. However, it requires an explicit model of a controlled target. In this research, we propose a motion planning method in which a system model is estimated by using Gaussian process regression. We apply our method to the control of an actual robot. Experimental results show that the control of the robot can be achieved by the proposed motion planning method.

Estimation of physical interaction between a musculoskeletal robot and its surroundings

Recently, robots are expected to support our daily lives in real environments. In such environments, however, there are a lot of obstacles and the motion of the robot is affected by them. In this research, we develop a musculoskeletal robotic arm and a system identification method for coping with external forces while learning the dynamics of complicated situations, based on Gaussian process regression (GPR). The musculoskeletal robot has the ability to cope with external forces by utilizing a bio-inspired mechanism. GPR is an easy-to-implement method, but can handle complicated prediction tasks. The experimental results show that the behavior of the robot while interacting with its surroundings can be predicted by our method.

HUMA: A human-like musculoskeletal robot platform for physical interaction studies

In a real environment, robots must handle contact with various objects. However, it is hard to model the contacts in advance, since there are a huge variety of objects in our daily lives. Humans have the ability to handle such physical interactions in daily life and such an ability is realized by adapting the physical characteristics produced by the skeletal structure with large DoFs and its actuating system with redundant muscles. In this research, to realize the adaptability of the physical characteristics of a robotic system with commodity type mechanical parts, we developed an actuator network system (ANS) where the motion of multiple actuators are bound by mutually inter-connection. By implementing this system, the response of the robot against various external forces can be modulated or a joint with very large moving range can be realized. In this report, we produced a compliant human-like upper body robot using ANS and investigated the effect of it on the physical characteristic and then the feasibility of the data-driven prediction of the contact force requisite for physical interaction.

Adaptive LSH based on the particle swarm method with the attractor selection model for fast approximation of Gaussian process regression

Gaussian process regression (GPR) is one of the non-parametric methods and has been studied in many fields to construct a prediction model for highly non-linear system. It has been difficult to apply it to a real-time task due to its high computational cost but recent high-performance computers and computationally efficient algorithms make it possible. In our previous work, we derived a fast approximation method for GPR using a locality-sensitive hashing (LSH) and product of experts model, but its performance depends on the parameters of the hash functions used in LSH. Hash functions are usually determined randomly. In this research, we propose an optimization method for the parameters of hash functions by referring to a swarm optimization method. The experimental results show that accurate force estimation of an actual robotic arm is achieved with high computational efficiency.

Investigation on dynamics of group decision making with collaborative web search

In this paper, we present results of investigation on the dynamics of group decision making - how people discuss and make a decision-with collaborative web search. Prior works proposed systems that support group decision making with web search but have not examined the influence of discussion behaviors especially on the satisfaction levels with the final conclusion. In this study, we conducted a set of experiments to observe discussion behaviors and the consequent satisfaction with the conclusion using our experimental system and a set of questionnaires. The task for each participant was to make a decision on a restaurant. Our primary results revealed (1) the similar activities across all groups at the beginning and the end of the group discussion, (2) a lack of correspondence between the satisfaction with the conclusion and the time spent to reach the conclusion, and (3) the presumption that a member who actively engaged in the activities that were visible for the other members was likely to be voted as a leader in the group discussion beyond the discussion. Finally, we discussed how to implement intelligent systems that aid group decision making.