We discuss the precise cooperative motion of a dual manipulator. In the inverse kinematics of cooperative redundant manipulators, a hierarchical method using null space and an optimization method prioritizing the end-effectors relative position in the objective function have been proposed. However, there is no guarantee that the relative position will be maintained in regions subject to joint limits and task-space reachability constraints. As a result, unacceptable errors may occur, and some tasks cannot be accomplished. We propose designing the maximum permissible errors in advance by expressing the target relative position as inequality constraints in the Quadratic Programming (QP) problem. By extending its description to include a virtual spring, we have also achieved subtle force application by two cooperated manipulators. The proposed method was verified by simulation and experiments.

Collaborative robots can relief human operators from excessive efforts during payload lifting activities. Modelling the human partner allows the design of safe and efficient collaborative strategies. In this paper, we present a control approach for human-robot collaboration based on human monitoring through whole-body wearable sensors, and interaction modelling through coupled rigid-body dynamics. Moreover, a trajectory advancement strategy is proposed, allowing for online adaptation of the robot trajectory depending on the human motion. The resulting framework allows us to perform payload lifting tasks, taking into account the ergonomic requirements of the agents. Validation has been performed in an experimental scenario using the iCub3 humanoid robot and a human subject sensorized with the iFeel wearable system.

A multi-fingered hand that can grasp and manipulate a variety of objects is an option for assisting people in their daily lives. However, the range of torque output that can be handled by the multi-fingered hand is very limited compared to the capability of the human hand. In this paper, we introduce a new multi-fingered hand consisting of a dynamic pulley and a linkage mechanism, aiming to achieve a human-like output torque with a human-like size. The proposed multi-fingered hand can achieve a fingertip force of 50N, which is equivalent to that of a human, and at the same time can perform delicate operations such as picking up a coin on a desk. In addition, we realized the stay-on-tab opening task of a can by utilizing fingertip strength.

Humans show specialized strategies for efficient collaboration. Transferring similar strategies to humanoid robots can improve their capability to interact with other agents, leading the way to complex collaborative scenarios with multiple agents acting on a shared environment. In this paper we present a control framework for robot-robot collaborative lifting. The proposed shared controller takes into account the joint action of both the robots thanks to a centralized controller that communicates with them, and solves the whole-system optimization. Efficient collaboration is ensured by taking into account the ergonomic requirements of the robots through the optimization of posture and contact forces. The framework is validated in an experimental scenario with two iCub humanoid robots performing different payload lifting sequences.

It is necessary to use not only foot but also hand, knee and other body parts to support body weight for locomotion in uneven terrain. Such multi-contact motion planning is an important research topic including lots of previous works; however, a problem of computational speed of planning is still remaining. In this paper, we propose a learning-based algorithm to speed up the planning. The algorithm reduces replanning of contact states by learning an evaluation function of key pose to reach goal. We investigated the learning performance by comparing three neural network configurations and two activation function. This research aims at achieving robust robotics system in unknown environments.

Realization of rigid and sensitive torque sensor is one of the key factors for the success of robots. With the conventional detectors as strain gauges, poor S/N (signal to noise) ratio has been the limitation of torque sensor sensitivity. Torque Encoder uses a linear encoder as a detector and significantly enhanced the S/N ratio, and realized stiff and sensitive torque sensor. However, the crosstalk in torque measurement was still an open problem as in other sensing methods. In this paper, we analyzed the cause of the crosstalk and proposed the mechanism to suppress the crosstalk using selectively compliant suspension mechanism. Design methodology, implementation to minimize crosstalk with minimal sensitivity loss are presented. Evaluation on a prototype of the mechanism is carried out to show the effectiveness of the mechanism.

When robots cooperate with humans it is necessary for robots to move safely on sudden impact. Joint torque sensing is vital for robots to realize safe behavior and enhance physical performance. Firstly, this paper describes a new torque sensor with linear encoders which demonstrates electro magnetic noise immunity and is unaffected temperature changes. Secondly, we propose a friction compensation method using a disturbance observer to improve the positioning accuracy. In addition, we describe a torque feedback control method which scales down the motor inertia and enhances the joint flexibility. Experimental results of the proposed controller are presented.