This paper presents a manipulation planning algorithm for robots to reorient objects. It automatically finds a sequence of robot motion that manipulates and prepares an object for specific tasks. ...Examples of the preparatory manipulation planning problems include reorienting an electric drill to cut holes, reorienting workpieces for assembly, and reorienting cargo for packing, etc. The proposed algorithm could plan single- and dual-arm manipulation sequences to solve the problems. The mechanism under the planner is a regrasp graph, which encodes grasp configurations and object poses. The algorithms search the graph to find a sequence of robot motion to reorient objects. The planner is able to plan both single- and dual-arm manipulation. It could also automatically determine whether to use a single arm, dual arms, or their combinations to finish given tasks. The planner is examined by various humanoid robots like Nextage, HRP2Kai, HRP5P, etc., using both simulation and real-world experiments.
We report on the results of a collaborative project that investigated the deployment of humanoid robotic solutions in air-craft manufacturing for several assembly op erations where access by wheeled ...or railported robotic platforms is not possible. Recent de velopments in multicontact planning and control, bipedal walking, embedded simultaneous localization and mapping (SLAM), whole-body multisensory task-space optimization control, and contact detection and safety suggest that humanoids could be a plausible solution for automation, given the specific requirements in large-scale manufacturing sites. The main challenge is the integration of these scientific and technological advances into two existing humanoid platforms: the position-controlled Human Robotics Project (HRP-4) and the torque-controlled robot (TORO). This integration effort was demonstrated during a bracket-assembly operation inside a 1:1-scale A350 mockup of the front part of the fuselage at the Airbus Saint-Nazaire site. We present and discuss the main results achieved in this project and provide recommendations for future work.
Recent developments in robotics have enabled humanoid robots to be used in tasks where they have to physically interact with humans, including robot-supported caregiving. This interaction-referred to ...as physical human-robot interaction (pHRI)-requires physical contact between the robot and the human body; one way to improve this is to use efficient sensing methods for the physical contact. In this paper, we use a flexible tactile sensing array and integrate it as a tactile skin for the humanoid robot HRP-4C. As the sensor can take any shape due to its flexible property, a particular focus is given on its spatial calibration, i.e., the determination of the locations of the sensor cells and their normals when attached to the robot. For this purpose, a novel method of spatial calibration using B-spline surfaces has been developed. We demonstrate with two methods that this calibration method gives a good approximation of the sensor position and show that our flexible tactile sensor can be fully integrated on a robot and used as input for robot control tasks. These contributions are a first step toward the use of flexible tactile sensors in pHRI applications.
Multi-contact motion is important for humanoid robots to work in various environments. We propose a centroidal online trajectory generation and stabilization control for humanoid dynamic ...multi-contact motion. The proposed method features the drastic reduction of the computational cost by using preview control instead of the conventional model predictive control that considers the constraints of all sample times. By combining preview control with centroidal state feedback for robustness to disturbances and wrench distribution for satisfying contact constraints, we show that the robot can stably perform a variety of multi-contact motions through simulation experiments.
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.
Recent advances in deep reinforcement learning (RL) based techniques combined with training in simulation have offered a new approach to developing robust controllers for legged robots. However, the ...application of such approaches to real hardware has largely been limited to quadrupedal robots with direct-drive actuators and light-weight bipedal robots with low gear-ratio transmission systems. Application to real, life-sized humanoid robots has been less common arguably due to a large sim2real gap. In this paper, we present an approach for effectively overcoming the sim2real gap issue for humanoid robots arising from inaccurate torque-tracking at the actuator level. Our key idea is to utilize the current feedback from the actuators on the real robot, after training the policy in a simulation environment artificially degraded with poor torque-tracking. Our approach successfully trains a unified, end-to-end policy in simulation that can be deployed on a real HRP-5P humanoid robot to achieve bipedal locomotion. Through ablations, we also show that a feedforward policy architecture combined with targeted dynamics randomization is sufficient for zero-shot sim2real success, thus eliminating the need for computationally expensive, memory-based network architectures. Finally, we validate the robustness of the proposed RL policy by comparing its performance against a conventional model-based controller for walking on uneven terrain with the real robot.
Humanoid robots are expected to evolve in complex environments performing parallel tasks including balance in multi-contact motion. Balance requires precise tracking of the contact forces, even in ...the presence of external disturbances. In this paper, we propose a framework to perform stabilization, force tracking, kinematic tasks, and disturbance-rejecting compliance with robots without joint torque feedback. The solution uses a QP with concurrent tasks to produce an inverse dynamics-based feed-forward torque together with kinematic feedback to achieve feasible Lyapunov-stable motions. The framework offers a range of task formulations and parameters as tools for fine force tracking, including an admittance-like task. This framework is tested in dynamic simulations with several locomotion scenarios in complex environments with continuous non-modeled disturbances.
This paper presents the open architecture humanoid robotics platform (OpenHRP) which consists of a simulator and motion control library of humanoid robots. The binary software developed on OpenHRP ...can be applied to the real counterpart as is, thank to the proposed hardware abstraction and synchronization mechanism. The compatibility between the simulation and corresponding experiment has been successfully examined. OpenHRP is expected to initiate the exploration of humanoid robotics on open architecture software and hardware.
Most simultaneous localization and mapping (SLAM) systems assume that SLAM is conducted in a static environment. When SLAM is used in dynamic environments, the accuracy of each part of the SLAM ...system is adversely affected. We term this problem as dynamic SLAM. In this study, we propose solutions for three main problems in dynamic SLAM: camera tracking, three-dimensional map reconstruction, and loop closure detection. We propose to employ geometry-based method, deep learning-based method, and the combination of them for object segmentation. Using the information from segmentation to generate the mask, we filter the keypoints that lead to errors in visual odometry and features extracted by the CNN from dynamic areas to improve the performance of loop closure detection. Then, we validate our proposed loop closure detection method using the precision-recall curve and also confirm the framework’s performance using multiple datasets. The absolute trajectory error and relative pose error are used as metrics to evaluate the accuracy of the proposed SLAM framework in comparison with state-of-the-art methods. The findings of this study can potentially improve the robustness of SLAM technology in situations where mobile robots work together with humans, while the object-based point cloud byproduct has potential for other robotics tasks.
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