No abstract

No abstract

We propose a framework for real-time online and offline retargeting of a human actor motion to a humanoid robot motion involving multi-contact configuration changes between the human/humanoid and their environments. The framework is based on the specification within a multi-contact QP control formulation of tracking tasks for a selected set of body-segments/links, the ones either used for a manipulation task from a fixed multi-contact stance or susceptible to be used as contact supports or as locomotion supports on the environment in the course of the execution of the motion. The framework is applied in an online setting for simultaneous human-robot motion tracking (in the case of no contact configuration change) and in an offline setting for tracking the playback of the recorded human motion that is pre-processed to extract from it the sequence of contact change events as a necessary motion information exploited by the tracking algorithm. We present a real robot experiment with HRP-4 for the online setting and a dynamics simulation experiment for the offline setting to validate the proposed approach

Robots artificially replicate human capabilities thanks to their software, the main embodiment of intelligence. However, engineering robotics software has become increasingly challenging. Developers need expertise from different disciplines as well as they are faced with heterogeneous hardware and uncertain operating environments. To this end, the software needs to be variable—to customize robots for different customers, hardware, and operating environments. However, variability adds substantial complexity and needs to be managed—yet, ad hoc practices prevail in the robotics domain, challenging effective software reuse, maintenance, and evolution. To improve the situation, we need to enhance our empirical understanding of variability in robotics. We present a multiple-case study on software variability in the vibrant and challenging domain of service robotics. We investigated drivers, practices, methods, and challenges of variability from industrial companies building service robots. We analyzed the state-of-the-practice and the state-of-the-art—the former via an experience report and eleven interviews with two service robotics companies; the latter via a systematic literature review. We triangulated from these sources, reporting observations with actionable recommendations for researchers, tool providers, and practitioners. We formulated hypotheses trying to explain our observations, and also compared the state-of-the-art from the literature with the-state-of-the-practice we observed in our cases. We learned that the level of abstraction in robotics software needs to be raised for simplifying variability management and software integration, while keeping a sufficient level of customization to boost efficiency and effectiveness in their robots’ operation. Planning and realizing variability for specific requirements and implementing robust abstractions permit robotic applications to operate robustly in dynamic environments, which are often only partially known and controllable. With this aim, our companies use a number of mechanisms, some of them based on formalisms used to specify robotic behavior, such as finite-state machines and behavior trees. To foster software reuse, the service robotics domain will greatly benefit from having software components—completely decoupled from hardware—with harmonized and standardized interfaces, and organized in an ecosystem shared among various companies.

No abstract