Industrial robot manipulators are playing a significant role in modern manufacturing industries. Though peg-in-hole assembly is a common industrial task that has been extensively researched, safely solving complex, high-precision assembly in an unstructured environment remains an open problem. Reinforcement-learning (RL) methods have proven to be successful in autonomously solving manipulation tasks. However, RL is still not widely adopted in real robotic systems because working with real hardware entails additional challenges, especially when using position-controlled manipulators. The main contribution of this work is a learning-based method to solve peg-in-hole tasks with hole-position uncertainty. We propose the use of an off-policy, model-free reinforcement-learning method, and we bootstraped the training speed by using several transfer-learning techniques (sim2real) and domain randomization. Our proposed learning framework for position-controlled robots was extensively evaluated in contact-rich insertion tasks in a variety of environments.

Reinforcement Learning (RL) methods have been proven successful in solving manipulation tasks autonomously. However, RL is still not widely adopted on real robotic systems because working with real hardware entails additional challenges, especially when using rigid position-controlled manipulators. These challenges include the need for a robust controller to avoid undesired behavior, that risk damaging the robot and its environment, and constant supervision from a human operator. The main contributions of this work are, first, we propose a fail-safe mechanism for safely training an RL agent on manipulation tasks using a rigid robot. Second, to enable a position-controlled manipulator to perform contact-rich manipulation tasks, we implemented two different force control schemes based on standard force feedback controllers; one is a modified parallel position-force control, and the other one is an impedance control. Third, we empirically study both control schemes when used as the action representation of an RL agent. We evaluate the trade-off between control complexity and performance by comparing several versions of the control schemes, each with a different number of force control parameters. The proposed methods are validated both on simulation and a real robot, a UR3 e-series robotic arm when executing contact-rich manipulation tasks.

This article identifies and summarizes software tools and frameworks proposed in the Human–Robot Interaction (HRI) literature for developing extended reality (XR) experiences using game engines. This review includes primary studies proposing Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) solutions where humans can control or interact with real robotic platforms using devices that extend the user’s reality. The objective of this article is not to present an extensive list of applications and tools. Instead, we present recent, relevant, common, and accessible frameworks and software tools implemented in research articles published in high-impact robotics conferences and journals. For this, we searched papers published during a seven-years period between 2015 and 2022 in relevant databases for robotics (Science Direct, IEEE Xplore, ACM digital library, Springer Link, and Web of Science). Additionally, we present and classify the application context of the reviewed articles in four groups: social robotics, programming of industrial robots, teleoperation of industrial robots, and Human–Robot collaboration (HRC).

This paper presents a brief review of affordance research in robotics, with special concentrations on its applications in grasping and manipulation of objects. The concept of affordance could be a key to realize human-like advanced manipulation intelligence. First, we discuss the concept of affordance while associating with the applications in robotics. Then, we intensively explore the studies that utilize affordance for robotic manipulation applications, such as object recognition, grasping, and object manipulation including tool-use. They obtain and use affordance by several ways like learning from human, using simulation, and real-world execution. Moreover, we show our current work, which is a cloud database for advanced manipulation intelligence. The database accumulates various data related to manipulation task execution and will be an open platform to leverage various affordance techniques.

Graphical Abstract This paper proposes a novel robotic gripper used for assembly tasks that can adaptively grasp objects with different shapes. The proposed hand has a combined structure between two kinds of shape adaptive mechanisms where one is the granular jamming and the other is a multi-finger mechanism driven by a single wire. Due to the effect of the two shape adaptive mechanisms, the pose of a grasped object does not change during an assembly operation. The proposed hand has four fingers where two are the active ones and the other two are the passive ones. The pose of the grasped object can be uniquely determined since the passive fingers are used to orient an object placed on a table before the active fingers are closed to grasp it. Assembly experiments of some kinds of parts are shown to validate the effectiveness of our proposed gripper.

This paper presents a method for planning motions of a flexible objects based on precise simulation using Finite Element Method (FEM). The proposed method is applied to ring-shape objects manipulated by robot arms, which is often seen in various applications. Since large deformation is implied, assembly planning with realistic simulation is important to ensure task efficiency for the robot and also to avoid damage of the object. We first verify that the behavior of a ring-shape object by dual-arm manipulation is well predicted using FEM model of bent beam through a simulation along the trajectory computed by optimization-based motion planning previously reported. Next, a precise FEM model is integrated into optimization to compute a trajectory of robot hands minimizing the deformation energy as well as satisfying such criteria as collision avoidance and smoothness. Since the direct computation leads huge computational cost, we present a realistic formula which transforms the planning problem into the static equilibrium problem of several FEM models located along the trajectory. Simulation results show that the proposed method is promising for such assembly tasks requiring large deformation.