Learning Robotic Assembly by Leveraging Physical Softness and Tactile Sensing

This study aims to achieve autonomous robotic assembly under uncertain conditions arising from imprecise goal positioning and variations in the angle of the grasped part. Soft robots are suitable for such uncertain and contact-rich environments and are capable of insertion tasks with imprecise goal positions. However, we may also struggle to handle further uncertainty, such as variations in grasping pose. To address the challenge posed by multiple sources of uncertainty, we equipped the soft robot with a tactile sensor. Our key insight is that tactile signal patterns are closely linked to the subtask transitions in an assembly process, specifically from the search to insertion subtasks. We hypothesize soft robots could complete the task by exploring the transition via tactile signals, even in scenarios with imprecise goal positions and grasp misalignment. To this end, we develop an anomaly detection model using a Variational Autoencoder to identify the timing of these transitions. We then employ learning and heuristic-based controllers to navigate the peg tip to the hole and perform the insertion. Our method was validated through real-robot experiments using a soft wrist and a vision-based tactile sensor. The results demonstrate that our method achieves a 100% success rate in scenarios with less uncertain goal pose (
$\sigma=2\text{mm}$
) and grasp misalignment (up to 5°) and a 70% success rate in scenarios with uncertain goal pose (
$\sigma=10\text{mm}$
) and grasp misalignment (up to 20°). Moreover, our anomaly detection model can generalize to different peg diameters without additional training.