Grasping fragile objects in the presence of un-certainty is a crucial task for robots, that becomes inherently challenging if the manipulator in use is an industrial robot platform that does not provide compliant control inputs. This requires not only to estimate the alignment error during object contact but also to alter the robot configuration to decrease this error while taking interaction constraints into account. Thus, this work proposes a novel grasping controller tailored to industrial robots by exploiting tactile sensor feedback on the robot gripper fingers in order to estimate and compensate for the alignment error when touching the object. Specifically, we propose two grasping strategies, that allow to either directly compensate for interaction wrenches or to solve a model predictive control-problem to minimize the estimated alignment error. Eventually, we outline how these modalities can be realized as a hybrid Cartesian force-velocity-controller on an industrial manipulator. We evaluate the proposed grasping strategies on a WSG 50 parallel two-finger gripper, that is equipped with a digital sensor array (DSA) per finger, for which we also provide an extended ROS-driver that allows to obtain DSA-data at a communication rate above 5 Hz. Given the collected empirical evidence, the presented grasping controller increases the skill-set of industrial robots in the presence of uncertainty and thus allows to apply stiff robots to handle fragile objects autonomously.

Redundant robots offer the possibility of improving agility, compared to their non-redundant counterparts, by exploiting the additional kinematic DOFs to increase a measure called manipulability. While it is common to maximize the manipulability measure during redundancy resolution locally, global optimization of a full trajectory is usually computationally too expensive and thus only considered for offline procedures in current literature. However, local maximization is prone to be sub-optimal and at times even fails at preserving agility of a robot that ought to be reactive. In this work we build upon our previous contributions on online trajectory generation on SE(3) and closed-form task space manipulability of a 7-DOF serial robot, and combine it with graph search techniques for global optimization. This enables, for the first time, online trajectory generation with globally optimal redundancy resolution regarding manipulability, to maintain agility in reactive robot behavior.

With the integration of Human-Robot Collaboration (HRC) in industrial assembly scenarios, robot systems face numerous challenges. In contrast to classic robot systems which follow a pre-programmed and fixed sequence of actions, an interaction scenario with humans in the loop requires mutual adaptation. In this paper a framework based on game theory is presented that allows robots to choose appropriate actions with respect to the action of human coworkers when collaborating in close proximity. The proposed framework models HRC scenarios as iterative games and selects action-strategies for the Human-Robot Team (HRT) by finding the Nash-Equilibria (NEs) of these games. In contrast to most common approaches, our proposed HRC-game treats the decision-making behavior equally for all agents involved. Therefore, the concept of game theory is applied to evaluate the mutual interference of all actions on the HRT to obtain pareto-optimal NEs, i.e. team-optimal action-allocations. The general framework of the proposed HRC-game is realized on an interactive pick-and-place scenario in close proximity. This exemplary HRC-game is tested in a human subject experiment of a KUKA LWR 4+ robot and a human coworker assembling toy-bricks in close proximity. The experimental measurements and statistically significant improvements in the subjective feedback hold as a proof-of-concept of the proposed HRC-game model.

With the increasing demand of humans and robots collaborating in a confined workspace, it is essential that robots can react instantaneously to unforeseen events while respecting comfort of human co-workers. As the human pays especial attention to the end-effector movement rather than individual joints in such tasks, we argue that it is necessary to limit exactly these end-effector dynamics directly on SE(3), regardless of any specific coordinate system. Only then it is possible to constrain the robot end-effector in its absolute dynamic values, to enhance human comfort. Common online trajectory generators (OTGs) are either limited to single degrees of freedom (DOFs) or consider decoupled multi-DOF mainly in joint space. While these strategies can be directly applied to Cartesian coordinate systems by using Euler angles for orientation, constraining the change in Euler angles does not lead to the correct angular velocities. In this work we present a novel OTG with constraints on translational and rotational magnitudes on SE(3). Simulations as well as experiments on a KUKA LWR4+ show the potential contribution towards improved Human-Robot-Collaboration.