Multi-Object Grasping -- Generating Efficient Robotic Picking and Transferring Policy
This addresses efficiency in robotic picking and transferring tasks for applications like logistics or manufacturing, though it is incremental as it builds on existing single-object approaches.
The paper tackles the problem of transferring multiple objects between bins more efficiently by enabling a robotic hand to grasp and transfer multiple objects at once, reducing the number of transfers by 59% and lifts by 58% compared to single-object methods.
Transferring multiple objects between bins is a common task for many applications. In robotics, a standard approach is to pick up one object and transfer it at a time. However, grasping and picking up multiple objects and transferring them together at once is more efficient. This paper presents a set of novel strategies for efficiently grasping multiple objects in a bin to transfer them to another. The strategies enable a robotic hand to identify an optimal ready hand configuration (pre-grasp) and calculate a flexion synergy based on the desired quantity of objects to be grasped. This paper also presents an approach that uses the Markov decision process (MDP) to model the pick-transfer routines when the required quantity is larger than the capability of a single grasp. Using the MDP model, the proposed approach can generate an optimal pick-transfer routine that minimizes the number of transfers, representing efficiency. The proposed approach has been evaluated in both a simulation environment and on a real robotic system. The results show the approach reduces the number of transfers by 59% and the number of lifts by 58% compared to an optimal single object pick-transfer solution.