In this paper, we propose a trajectory planning framework for a robot that exploits a pre-computed database of end-effector trajectories as the guidance of optimization-based inverse kinematics. We constructed a reachable graph of a robot offline, which represents feasible end-effector paths with corresponding configurations. When performing the online trajectory planning, we applied A* search to the reachable graph to find a feasible path between input start and goal globally in the task space. Its cost function has the separated term dependent on the robot, which comes from the manipulability of configurations preserved in the reachable graph, and that is dependent on the environment. Then, we solve optimization-based inverse kinematics to generate an optimal joint trajectory while utilizing the end-effector trajectory and its corresponding configurations as the guidance to avoid local optimum. We evaluated our framework quantitatively by comparing it with existing methods to confirm that it achieved a high success rate and quality of results while suppressing its computational time. We also qualitatively proved its practicality by applying it to the material handling task in the real-world. This result shows that it improved the performance of the optimization-based inverse kinematics avoiding local optimum and applicability to the different environments of the pre-computed motion database.

This paper focuses on a stabilization control for multi-contact motion which enables a humanoid robot to locomote by realizing dynamic contact transitions on non-flat environment. In the stabilization process of the multi-contact motion, the desired Zero-Moment Point (ZMP) is modified by the position of the Divergent Component of Motion (DCM) error with respect to the 3D Center of Mass (CoM) motion generated from the force distribution ratio. The contact wrench of each end-effector is determined by quadratic optimization considering the centroidal dynamics and contact friction constraints so as to satisfy the modified ZMP. Each end-effector is controlled by optimized force reference through a projection of null space by force distribution ratio. We propose a multi-contact stabilization framework which can be designed not only to generate 3D CoM motion but also the CoM position estimation and the optimal force distribution around the reference ZMP in a unified manner from a balance controller, by using the force distribution ratio. The effectiveness of proposed method is validated by a quadruped locomotion leaning against a vertical wall using the joint position controlled humanoid HRP-5P in a dynamic simulator.

In order to accurately reach a target position while executing a task which imposes occlusion or constraints of the posture, a humanoid robot requires an adaptive locomotion system, which can comprehensively integrate localization, environmental mapping, global locomotion planning and local error correction. In this paper, we propose a method of constructing a perception based locomotion system for a humanoid robot. The major contribution of this paper is solving a problem of the locomotion error caused by the task constraints, by locally compensating footsteps and assessing the need for global footstep re-planning online based on environmental measurements. The proposed system provides an accurate and dense ground point cloud, called HeightField, using plane estimation and space interpolation, and obstacle point cloud for frequent collision avoidance by accumulating laser scans. This environmental perception enables a humanoid robot to plan footsteps globally even in the situation where the sight of the robot is limited and compensate footsteps while estimating landing state during locomotion online with the localization result. We evaluated the practicality of the proposed system by applying it to our humanoid robot carrying a heavy object in a construction site and confirmed that the proposed system contributed to improved locomotion abilities of a humanoid robot engaging in heavy-duty or dangerous tasks.

To achieve tasks in unknown environments with high reliability, highly accurate localization during task execution is necessary for humanoid robots. In this paper, we discuss a localization system which can be applied to a humanoid robot when executing tasks in the real world. During such tasks, humanoid robots typically do not possess a referential to a constant horizontal plane which can in turn be used as part of fast and cost efficient localization methods. We solve this problem by first computing an improved odometry estimate through fusing visual odometry, feedforward commands from gait generator and orientation from inertia sensors. This estimate is used to generate a 3D point cloud from the accumulation of successive laser scans and such point cloud is then properly sliced to create a constant height horizontal virtual scan. Finally, this slice is used as an observation base and fed to a 2D SLAM method. The fusion process uses a velocity error model to achieve greater accuracy, which parameters are measured on the real robot. We evaluate our localization system in a real world task execution experiment using the JAXON robot and show how our system can be used as a practical solution for humanoid robots localization during complex tasks execution processes.

Object handover is a basic task in many human-robot interactive scenarios and therefore, it is important for assistive robots to be able to perform proper handovers. We previously designed a human-inspired grip-force-varying handover controller for a robot giver and showed on a Willow Garage PR2 robot that the controller yields human-like and human-preferred handovers. The PR2 robot had a non-compliant fully-actuated gripper. However, recently, compliant underactuated grippers have been gaining more popularity. Although compliant underactuated grippers can provide more flexibility in manipulation, it is generally difficult to accurately measure and control the amount of applied grip force. In this paper, we present an implementation of the human-inspired handover controller on a Kawada Industries HRP4R robot, which has compliant underactuated hands, using joint position error measurement for estimating the amount of applied grip force. Through an experiment, we show that we are able to achieve safe, smooth, and intuitive robot-human handovers despite the lack of accurate grip force control on our robot.

Manipulation of structured objects connected to the environment by a kinematics chain involves two problems: (a) The objects have movable directions and unmovable directions. An undesired reaction force in the unmovable directions prevents a robot from successful manipulation; (b) The reaction forces from the objects could fluctuate during manipulation. Related works have enabled robots to manipulate objects by integrating position control in movable directions and force control in unmovable directions at the hands. However, in the case of a humanoid robot, too large undesired reaction forces in movable directions cause the robot's falling down and slipping. In this paper, we propose a controller system controlling reaction forces at the hands and successively updating reference forces based on reaction forces. For problem (a), we apply force control both to the movable and unmovable directions in order to satisfy both maintaining full-body balance and achieving manipulation. For problem (b), the update of the reference forces enables the humanoid robot to adapt to fluctuation of the reaction forces. We show experimental results on the cmanipulating four doors and a drawer.