Online Tool Selection with Learned Grasp Prediction Models

TL;DR

This paper introduces a planning approach for robotic bin-picking that optimizes tool change sequences using learned grasp models and MDP-based decision making, improving throughput despite occlusion uncertainties.

Contribution

It presents a novel MDP-based framework with void zones and approximate ILP solutions for efficient tool-change planning in robotic bin-picking.

Findings

Sparse tree search achieves near-optimal performance.

Throughput-based metrics may overlook smoothness of actions.

Method is validated on synthetic and real-world tasks.

Abstract

Deep learning-based grasp prediction models have become an industry standard for robotic bin-picking systems. To maximize pick success, production environments are often equipped with several end-effector tools that can be swapped on-the-fly, based on the target object. Tool-change, however, takes time. Choosing the order of grasps to perform, and corresponding tool-change actions, can improve system throughput; this is the topic of our work. The main challenge in planning tool change is uncertainty - we typically cannot see objects in the bin that are currently occluded. Inspired by queuing and admission control problems, we model the problem as a Markov Decision Process (MDP), where the goal is to maximize expected throughput, and we pursue an approximate solution based on model predictive control, where at each time step we plan based only on the currently visible objects. Special to our method is the idea of void zones, which are geometrical boundaries in which an unknown object will be present, and therefore cannot be accounted for during planning. Our planning problem can be solved using integer linear programming (ILP). However, we find that an approximate solution based on sparse tree search yields near optimal performance at a fraction of the time. Another question that we explore is how to measure the performance of tool-change planning: we find that throughput alone can fail to capture delicate and smooth behavior, and propose a principled alternative. Finally, we demonstrate our algorithms on both synthetic and real world bin picking tasks.