The SAHR project has significantly advanced our understanding of human skill acquisition. It conducted the first in-situ longitudinal study of watchmaking apprentices, providing unique, detailed measurements of finger motion and pressure. These real-world data differ from traditional lab-based studies and offer a historical account of watchmaking, a highly complex craft.
The SAHR project offered new insight on human dexterity. Specifically, we documented a novel taxonomy of bimanual hand postures, revealing the role of handedness in fine manipulation. While handedness is well documented for arm movements, its role in fine manipulation has received much less attention.Moreover our work provided evidence that, while humans rarely exploit the full potential of the hands and fingers dexterity, they can do so easily when the task requires to go beyond the usual routine.
The SAHR project contributed important theoretical and experimental advances in machine learning and robotics. It pushed forward the use of dynamical systems as a mode to control robots. While learning dynamical systems was so far performed in a fully supervised manner, results from SAHR show that unsupervised learning techniques can also be used for the identification of the systems. This is advantageous, as the approach does not necessitate explicit labelling which is time consuming and relies on expert data.
The SAHR project was interdisciplinary, with human behavior studies informing robot controller design. Observations from human skill acquisition, such as prioritizing feasibility over optimality, influenced the design of algorithms and robot controllers.
Ultimately, the SAHR project achieved its goal, producing robots with high dexterity. Examples include a robotic hand picking up and holding four objects simultaneously, skillfully balancing a champagne glass, and rotating everyday objects such as a pencil, fork, cable and banana with its fingers.
