Formulating Intuitive Stack-of-Tasks using Visuo-Tactile Perception for Collaborative Human-Robot Fine Manipulation
This work addresses the need for adaptable and flexible collaborative robots in human-robot interaction scenarios, though it appears incremental by building on existing stack-of-tasks methods with sensory enhancements.
The researchers tackled the problem of enabling robots to collaborate closely with humans in fine manipulation tasks by proposing an intuitive stack-of-tasks formulation augmented with visuo-tactile perception, resulting in improved performance in assembly and disassembly tasks as measured by metrics like task coordination latency and cumulative posture deviation.
Enabling robots to work in close proximity to humans necessitates a control framework that does not only incorporate multi-sensory information for autonomous and coordinated interactions but also has perceptive task planning to ensure an adaptable and flexible collaborative behaviour. In this research, an intuitive stack-of-tasks (iSoT) formulation is proposed, that defines the robot's actions by considering the human-arm postures and the task progression. The framework is augmented with visuo-tactile information to effectively perceive the collaborative environment and intuitively switch between the planned sub-tasks. The visual feedback from depth cameras monitors and estimates the objects' poses and human-arm postures, while the tactile data provides the exploration skills to detect and maintain the desired contacts to avoid object slippage. To evaluate the performance, effectiveness and usability of the proposed framework, assembly and disassembly tasks, performed by the human-human and human-robot partners, are considered and analyzed using distinct evaluation metrics i.e, approach adaptation, grasp correction, task coordination latency, cumulative posture deviation, and task repeatability.