Interactive Structure Robotic Repositioning of Vertical Elements in Man-Machine Collaborative Assembly through Vision-Based Tactile Sensing

Abstract

The research presented in this paper explores a novel tactile sensor technology for architectural assembly tasks. In order to enable robots to interact both with humans and building elements, several robot control strategies had to be implemented. Therefore, we developed a communication interface between the architectural design environment, a tactile sensor and robot controllers. In particular, by combining tactile feedback with real-time gripper and robot control algorithms, we demonstrate grasp adaptation, object shape and texture estimation, slip and contact detection, force and torque estimation. We investigated the integration of robotic control strategies for human-robot interaction and developed an assembly task in which the robot had to place vertical elements underneath a deformed slab. Finally, the proposed tactile feedback controllers and learned skills are combined together to demonstrate applicability and utility of tactile sensing in collaborative human-robot architectural assembly tasks. Users were able to hand over building elements to the robot or guide the robot through the interaction with building elements. Ultimately this research aims to offer the possibility for anyone to interact with built structures through robotic augmentation.

CoBuilt 4.0: Investigating the potential of collaborative robotics for subject matter experts

“…15 However, machine learning process for training paths/movements is a viable alternative since knowledge in this domain has advanced to industrial robots already being trained to learn how to perform a task, such as current automated, servicing humanoid applications, 16 or collaborative repositioning in robotichuman assembly. 17 Thus, as direct extension of the pilot studies and structured investigation into action protocols, the research explored methods for data capturing processes, including movement tracking vs motion programming or machine learning. This included the establishment of a methodology for workflow capture and analysis of carpentry tasks towards human-robot collaboration in the case study investigations; and a framework that outlines two pathways of training robots through machine learning -supervised and reinforcement learning (building on previous research including Brynolfsson et al, 18 Shalev-Shwartz and Ben-David, 19 Michalski et al 20 ).…”

“…15 However, machine learning process for training paths/movements is a viable alternative since knowledge in this domain has advanced to industrial robots already being trained to learn how to perform a task, such as current automated, servicing humanoid applications, 16 or collaborative repositioning in robotic-human assembly. 17…”