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Technologies from open source projects have seen widespread adoption in robotics in recent years. The rapid pace of progress in robotics is in part fueled by open source projects, providing researchers with resources, tools, and devices to implement novel ideas and approaches quickly. Open source hardware, in particular, lowers the barrier of entry to new technologies and can further accelerate innovation in robotics. But open hardware is also more difficult to propagate in comparison to open software because it involves replicating physical components, which requires users to have sufficient familiarity and access to fabrication equipment. In this work, we present a review on open robot hardware (ORH) by first highlighting the key benefits and challenges encountered by users and developers of ORH, and then relaying some best practices that can be adopted in developing successful ORH. To accomplish this, we surveyed more than 80 major ORH projects and initiatives across different domains within robotics. Finally, we identify strategies exemplified by the surveyed projects to further detail the development process, and guide developers through the design, documentation, and dissemination stages of an ORH project.

Robots are rapidly becoming part of our lives, coexisting, interacting, and collaborating with humans in dynamic and unstructured environments. Mapping of human to robot motion has become increasingly important, as human demonstrations are employed in order to “teach” robots how to execute tasks both efficiently and anthropomorphically. Previous mapping approaches utilized complex analytical or numerical methods for the computation of the robot inverse kinematics (IK), without considering the humanlikeness of robot motion. The scope of this work is to synthesize humanlike robot trajectories for robot arm-hand systems with arbitrary kinematics, formulating a constrained optimization scheme with minimal design complexity and specifications (only the robot forward kinematics (FK) are used). In so doing, we capture the actual human arm-hand kinematics, and we employ specific metrics of anthropomorphism, deriving humanlike poses and trajectories for various arm-hand systems (e.g., even for redundant or hyper-redundant robot arms and multifingered robot hands). The proposed mapping scheme exhibits the following characteristics: (1) it achieves an efficient execution of specific human-imposed goals in task-space, and (2) it optimizes anthropomorphism of robot poses, minimizing the structural dissimilarity/distance between the human and the robot arm-hand systems.