Grasping is the process of picking up an object by applying forces and torques at a set of contacts. Recent advances in deep learning methods have allowed rapid progress in robotic object grasping. In this systematic review, we surveyed the publications over the last decade, with a particular interest in grasping an object using all six degrees of freedom of the end-effector pose. Our review found four common methodologies for robotic grasping: sampling-based approaches, direct regression, reinforcement learning, and exemplar approaches In addition, we found two “supporting methods” around grasping that use deep learning to support the grasping process, shape approximation, and affordances. We have distilled the publications found in this systematic review (85 papers) into ten key takeaways we consider crucial for future robotic grasping and manipulation research.

A major goal of humanoid robotics is to enable safe and reliable human-robot collaboration in realworld scenarios. In this article, we present ARMAR-6, a new high-performance humanoid robot for various tasks, including but not limited to grasping, mobile manipulation, integrated perception, bimanual collaboration, compliant-motion execution, and natural language understanding. We describe how the requirements arising from these tasks influenced our major design decisions, resulting in vertical integration during the joint hardware and software development phases. In particular, the entire hardware-including its structure, sensor-actuator units, and low-level controllers-as well as its perception, grasping and manipulation skills, task coordination, and the entire software architecture were all developed by one team of engineers. Component interaction is facilitated by our software framework ArmarX, which further facilitates the seamless integration and interchange of third-party contributions. To showcase the robot's capabilities, we present its performance in a challenging industrial maintenance scenario that requires human-robot collaboration, where the robot autonomously recognizes the human's need of help and offers said help in a proactive way.

The ability of humans to bimanually manipulate objects is unprecedented and has not been matched by robots yet. The goal-oriented coordination requires the consideration of both temporal and spatial constraints between the hands. Within this work, we propose a taxonomy which differentiates between the different coordination patterns observed in human bimanual manipulation. The taxonomy rests on the key aspects of coordination and interaction of the hands, hand roles and symmetry in the execution of bimanual tasks. To validate the taxonomy, we propose a contact- and graph-based representation of the task, which combined with a rule-based classification provides the category of bimanual actions.