Using meta-reasoning for incremental repairs in multi-object robot manipulation tasks

Robots tasked with object assembly by manipulation of parts require not only a high-level plan for order of placement of parts but also detailed low-level information on how to place and pick the part based on its state. This is a complex multi-level problem prone to failures at various levels. This paper employs meta reasoning architecture along with robotics principles and proposes dual encoding of state expectations during the progression of task to ground nominal scenarios. We present our results on table-top scenario using perceptual expectations based in the concept of occupancy grids and key point representations. Our results in a constrained manipulation setting suggest using low-level information or high-level expectations alone the system performs worse than if the architecture uses them both. We then outline a complete architecture and system which tackles this problem for repairing more generic assembly plans with objects moving in spaces with 6 degrees of freedom.

Automated summary

This paper explores the complex problem of robots assembling objects and discusses how to improve system performance by combining low-level information and high-level expectations. By using meta reasoning architecture and perceptual expectations, a dual encoding approach is proposed to determine nominal scenarios during task progress. Results show that in practice, considering both low-level information and high-level expectations performs better than using them separately.