The peg-in-hole assembly is a representative robotic task that involves physical contact with the external environment. In this paper, in contrast to past research in the area, which has involved the utilization of such expensive devices as force/torque sensors or remote compliance mechanisms, an inexpensive method is proposed for peg-in-hole assembly without force feedback or passive compliance mechanisms. The method consists of an analysis of the state of contact between the peg and the hole as well as a strategy to overcome the inevitable positional uncertainty of the hole incurred in the recognition process. A control scheme was developed to yield compliant behavior from the robot with physical contact under the condition of hybrid position/force control. The effectiveness of the proposed method was experimentally verified using a robot manipulator with 8-degrees of freedom and a peg-in-hole apparatus with a small clearance (0.1 mm).

This paper presents an anthropomorphic robotic hand named “KITECH-Hand,” along with its kinematic analysis and detailed mechanical features. From a kinematic perspective, the authors particularly focus on the structure of the metacarpophalangeal (MCP) joints of the fingers. The KITECH-Hand adopts a new “roll-pitch”-type MCP structure to replace conventional “yaw-pitch” structures. The proposed structure provides benefits such as enhanced kinematic performance and ease of the mechanical design. Through the kinematic analysis, it is shown that the KITECH-Hand shows remarkably high dexterity, well surpassing that of existing robotic hands with conventional MCP joints. The unique MCP structure also helps modularize the robot at the joint level, which simplifies its mechanical structure and enables the production cost to be reduced. The performance of the KITECH-Hand, including its dexterity feature, was experimentally verified through a series of experiments, which included object in-hand manipulation and a Cutkosky taxonomy test.

Since path planning for multi-arm manipulators is a complicated high-dimensional problem, effective and fast path generation is not easy for the arbitrarily given start and goal locations of the end effector. Especially, when it comes to deep reinforcement learning-based path planning, high-dimensionality makes it difficult for existing reinforcement learning-based methods to have efficient exploration which is crucial for successful training. The recently proposed soft actor–critic (SAC) is well known to have good exploration ability due to the use of the entropy term in the objective function. Motivated by this, in this paper, a SAC-based path planning algorithm is proposed. The hindsight experience replay (HER) is also employed for sample efficiency and configuration space augmentation is used in order to deal with complicated configuration space of the multi-arms. To show the effectiveness of the proposed algorithm, both simulation and experiment results are given. By comparing with existing results, it is demonstrated that the proposed method outperforms the existing results.

This study focuses on implementing robotic peg-in-hole in a more human-like approach, namely using dual-arms and dexterous robotic hands. The peg-in-hole strategy in this study mainly consists of two parts: grasping strategy (hand part) and assembly strategy (arm part). The grasping strategy explains the fundamental grasping method called “advanced blind grasping” and the in-hand manipulation method that is required for the reorientation of the workpiece. A feed-forward task space force control scheme is proposed for the actual implementation. The assembly strategy presents fundamental unit motions called “perturbation pattern” and proposes four assembly stages that are constructed by a combination of the unit motions. A force-position hybrid control for the implementation of the assembly strategy was also addressed. For evaluation, a peg-in-hole assembly demonstration with a keyhole-like shape was conducted using a human-sized 50-DOF upper-body robot.

This study proposes a mathematical uncertainty model for the spatial measurement of visual features using Kinect™ sensors. This model can provide qualitative and quantitative analysis for the utilization of Kinect™ sensors as 3D perception sensors. In order to achieve this objective, we derived the propagation relationship of the uncertainties between the disparity image space and the real Cartesian space with the mapping function between the two spaces. Using this propagation relationship, we obtained the mathematical model for the covariance matrix of the measurement error, which represents the uncertainty for spatial position of visual features from Kinect™ sensors. In order to derive the quantitative model of spatial uncertainty for visual features, we estimated the covariance matrix in the disparity image space using collected visual feature data. Further, we computed the spatial uncertainty information by applying the covariance matrix in the disparity image space and the calibrated sensor parameters to the proposed mathematical model. This spatial uncertainty model was verified by comparing the uncertainty ellipsoids for spatial covariance matrices and the distribution of scattered matching visual features. We expect that this spatial uncertainty model and its analyses will be useful in various Kinect™ sensor applications.