Autonomous robotic grasping of uncertain objects in uncertain environments is an impactful open challenge for the industries of the future. One such industry is the recycling of Waste Electrical and Electronic Equipment (WEEE) materials, in which electric devices are disassembled and readied for the recovery of raw materials. Since devices may contain hazardous materials and their disassembly involves heavy manual labor, robotic disassembly is a promising venue. However, since devices may be damaged, dirty and unidentified, robotic disassembly is challenging since object models are unavailable or cannot be relied upon. This case study explores grasping strategies for industrial robotic disassembly of WEEE devices with uncertain vision data. We propose three grippers and appropriate tactile strategies for force-based manipulation that improves grasping robustness. For each proposed gripper, we develop corresponding strategies that can perform effectively in different grasping tasks and leverage the grippers design and unique strengths. Through experiments conducted in lab and factory settings for four different WEEE devices, we demonstrate how object uncertainty may be overcome by tactile sensing and compliant techniques, significantly increasing grasping success rates.

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.

Gaussian Process (GP) formulation of continuous-time trajectory offers a fast solution to the motion planning problem via probabilistic inference on factor graph. However, often the solution converges to in-feasible local minima and the planned trajectory is not collision-free. We propose a message passing algorithm that is more sensitive to obstacles with fast convergence time. We leverage the utility of min-sum message passing algorithm that performs local computations at each node to solve the inference problem on factor graph. We first introduce the notion of compound factor node to transform the factor graph to a linearly structured graph. We next develop an algorithm denoted as Min-sum Message Passing algorithm for Motion Planning (MS2MP) that combines numerical optimization with message passing to find collision- free trajectories. MS2MP performs numerical optimization to solve non-linear least square minimization problem at each compound factor node and then exploits the linear structure of factor graph to compute the maximum a posteriori (MAP) estimation of complete graph by passing messages among graph nodes. The decentralized optimization approach of each compound node increases sensitivity towards avoiding obstacles for harder planning problems. We evaluate our algorithm by performing extensive experiments for exemplary motion planning tasks for a robot manipulator. Our evaluation reveals that MS2MP improves existing work in convergence time and success rate.

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.

Humans are error-prone in the presence of multiple similar tasks. While Human-Robot Collaboration (HRC) brings the advantage of combining the superiority of both humans and robots in their respective talents, it also requires the robot to communicate the task goal clearly to the human collaborator. We formalize such problems in interactive assembly tasks with hidden goal Markov decision processes (HGMDPs) to enable the symbiosis of human intention recognition and robot intention expression. In order to avoid the prohibitive computational requirements, we provide a myopic heuristic along with a feature-based state abstraction method for assembly tasks to approximate the solution of the resulting HGMDP. A user study with human subjects in round-based LEGO assembly tasks shows that our algorithm improves HRC and helps the human collaborators when the task goal is unclear to them.