Robot manipulation researchers reference human performance as a goal for their work, however, human data is seldom present in robotics benchmarks. We introduce a real-world benchmark targeting manipulation skills for performing electrical circuit inspection with a multimeter using an Internet-connected electronic task board. We present timing study results and an exemplary robot solution across six different tasks from the Robothon Grand Challenge at the automatica conference in 2023. Contributions from 16 robot teams were collected using task boards we manufactured and distributed as part of the 30-day international competition as an initial performance database. Our work systematically highlights the skill gap between the winning robot solution and the best human performance from a group of 30 subjects. Our goal is to chronicle progress over time in robot manipulation skills and provide a standardized, physical benchmark across the global community. Videos of the team submissions, the exemplary robot solution, as well as the project reproduction code are provided in the included repository.

The basis for robotics skill learning is an adequate representation of manipulation tasks based on their physical properties. As manipulation tasks are inherently invariant to the choice of reference frame, an ideal task representation would also exhibit this property. Nevertheless, most robotic learning approaches use unprocessed, coordinate-dependent robot state data for learning new skills, thus inducing challenges regarding the interpretability and transferability of the learned models.In this paper, we propose a transformation from spatial measurements to a coordinate-invariant feature space, based on the pairwise inner product of the input measurements. We describe and mathematically deduce the concept, establish the task fingerprints as an intuitive image-based representation, experimentally collect task fingerprints, and demonstrate the usage of the representation for task classification. This representation motivates further research on data-efficient and transferable learning methods for online manipulation task classification and task-level perception.

We present an accessible robot interface for telemanipulation (A-RIFT), which preserves the haptic channel partially in a zero-additional-cost interface by visual substitution of force feedback (VSFF). This work explores a gap in the literature, resulting from the focus on performance improvements in telerobotics at increasing interface costs. Unlike most telemanipulation interfaces for high-degree-of-freedom robotic systems, this one requires minimal training and can be run in a web browser under high latency conditions, using an Internet connected computer with the user's own mouse and keyboard. To evaluate the performance of the system, we ran a controlled user study (N=12) to test how different distances (local vs. remote) and VSFF (on vs. off) affect the system's usability. As expected, participants in remote conditions performed worse than those in closer proximity. Despite several participants claiming that the visual display of force feedback did not help them, our analysis of their task performance showed that operators in remote condition actually performed statistically significantly better with the visual force feedback display than without it. These results indicate a promising new interface design direction for low-cost telemanipulation.