Learning and Model-based Approaches in Logistics

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2 Learning and Model-based Approaches in Logistics Report from the field. Where are we now? Where are we going? Magazino GmbH Landsberger Str München T F info@magazino.eu

3 The future of Robotics To date, flexible automation is applicable in limited circumstances in logistics Highly automated production line at Audi Highly manual warehouse and logistics at the same Audi plant 47% of jobs replaced by robots and digitalisation in the next two decades (Oxford Study*) * Frey, C. B., & Osborne, M. A. (2013). The future of employment: how susceptible are jobs to computerisation. Retrieved September, 7,

4 Advanced robotics Advanced robotics is a quantum leap compared to conventional robotics Conventional robotics Advanced robotics Enables Magazino s robots + High precision & performance - Only repetitive, pre-defined tasks - Deterministic Camera & sensor based techniques Live decisions & adaption of behavior Learning via artificial intelligence Cloud based 4

5 Magazino s solution for intralogistics The customers problem Manual: 90% Eg. Zalando: ~1,500 Picker/warehouse Automated: 10% Fully automated small part warehouse High labor & process costs Dependence on staff Missing flexibility Missing scalability High initial investment 5

6 Toru A mobile pick & place robot for warehouse logistics 6

7 Toru 7

8 Magazino s solution for intralogistics Magazino s robot TORU enables flexible automation and reduces picking costs The customers problem Magazino s solution Manual: 90% Eg. Zalando: ~1,500 Picker/warehouse Automated: 10% Fully automated small part warehouse TORU for the fulfilment: In operation TORU for the material supply: In development High labor & process costs Dependence on staff Missing flexibility Missing scalability High initial investment Reduced costs of labor and processes Integration into existing warehouse Item specific handling Work alongside people New logistic concepts possible 8

9 Technology development AI driven robots represent the future of Magazino Future Present Perception + AI driven robots Past PLC Perception-driven robots PLC-based automation Designed for deterministic automation Programming at the coordinate- and motion level Perception and on-line decision making for managing the uncertainty of the world Enables flexible automation applications, e.g. picking of cuboid objects Automatic adaptation to changing conditions and dynamic environments Improved performance by learning optimal recognition models and control parameters Fast deployment and long-term robustness 10

10 Inside Toru s World 12

11 Making use of all the data TORU collects Data sources Representation & Synchronization Learning & Reasoning Generalization Perception Grasping Navigation Safety Environment model Picking order lists Common information representation + automated reasoning Detect objects more reliably Learn object-specific grasps Adapt navigation to changes in the warehouse Improve HRI and safety by predicting human movements Deploy robots faster to new warehouses Compute detailed performance statistics 13

12 AI projects at Magazino Behavior Tree as abstract execution model Learned cost models for multirobot task allocation Learned models for estimating the battery state Classifier for deciding if a shelf compartment is empty A good model of the robot s behavior helps to manage the complexity in real-world manipulation tasks The decision which robots perform which picks in which order has huge impact on the overall performance Battery discharge profiles are nonlinear, so a prediction of the remaining charge is difficult Scanning a compartment to check if it is empty can take a long time Behavior trees as execution model allow for easy creation, modification, visualization, execution, introspection and debugging Learning models in a datadriven approach can increase cost prediction accuracy by up to 96% Better predictions of the remaining charge improve operation time and reduce the risk of empty batteries A classifier using camera images can speed up this decision up to 5x 14

13 Conclusions Mobile pick & place robots that operate in open environments alongside humans require strong perceptual and decision-making abilities to perform their tasks robustly To adapt and generalize, a model-based control approach is applied Learning and reasoning promise to further improve performance, robustness, and adaptivity 15

14 Help us revolutionize warehouse logistics! Join the Team! We are looking for people to work with us or invest into us Contact MAGAZINO GmbH Landsberger Str München Germany Robotics Engineer Mechanical Engineer Electrical Engineer Robotics Project Manager Senior Sales Manager 16