# High-Speed Autonomous Robotic Assembly Using In-Hand Manipulation and Re-Grasping
## 2 Related Work  
In this work, we present an integrated robotic assembly system that can perceive the environment, plan for the assembly sequence, feasible grasps and manipulator motion and control the hardware in real time. The system is deployed and tested for reliability and performance beyond controlled laboratory environment. As such a system requires multiple components to work together reliably, previous works mostly focus on individual components rather than an integrated system. In [3], the authors provided a summary of recent robot manipulation research topics, which include robotic hand design, perception for manipulation, grasping, manipulation and approaches to utilize machine learning for manipulation, but most of the works presented are focused on individual components. For example, in [4], the authors focused on assembly sequence and grasp planning for assembly tasks, and, in [5; 6], the authors used deep learning based approach to learn grasp planning strategy from data acquired by repeated trials.  
There have been a few works that address an integrated system that can perceive, plan and control its manipulators for assembly tasks. In [7], the authors presented an integrated system for autonomous robotic manipulation that integrates 3D perception, motion planning and real-time motion control. In [8], the authors presented a robot system that has 2D stereo vision camera to detect target objects and uses two 7 DOF arms for a simple assembly task. In [9], the authors presented an integrated robotic system for autonomous bin picking task, which integrates the 3D perception, grasp and arm motion planning and motion control of the hardware. The system has been validated in public competition, the Amazon Robotics Challenge, and has shown exceptional performance in terms of reliability, accuracy and speed.  
Recently, there have been a number of works using the Soma cube assembly task as a demonstration [10; 11; 12; 13; 14]. The Soma cube assembly task is a good benchmark task for robotic assembly as it has nontrivial number of possible assembly solutions and grasp plans, yet most of the assembly can be realized using a single robotic manipulator. In [14], the authors focused on finding robust grasping strategies by 2-D form closure with cylindrical fingers for arbitrarily shaped objects. The suggested algorithm has been demonstrated by assembling Soma cube blocks into a cube.  
In [10], the authors proposed an integrated planning system that plans for the assembly sequence and motion to stack a Soma cube block on another. Although the suggested work is close to this work as it integrates the assembly sequence planning, motion planning and re-grasping required for the Soma cube assembly task, the suggested system is largely limited as it relies upon full information of the blocks and the assembly is limited for only two blocks. The authors extended the suggested system in [11], which allows for stacking up to five blocks. In [13], the authors proposed a planner that plans the optimal assembly sequence of Soma cube blocks for a dual armed robot. Given the mesh model of objects and the final assembly configuration, the suggested algorithm finds an optimal assembly sequence for dual armed robot considering the stability, graspability and assemblability. Finally, recently, in [12], the authors proposed the single-arm motion planner for Soma cube block assembly, which, given the initial and final pose of a Soma cube block, automatically generates the collision free arm motion plan with optional re-grasping to assemble the blocks incrementally.