Robotic Packaging Optimization with Reinforcement Learning and Real-World Data

Intelligent manufacturing has become increasingly important in the food packaging industry due to the growing demand for enhanced productivity and flexibility while minimizing waste and lead times. This work explores the integration of such manufacturing in automated secondary robotic food packaging solutions that transfer food products into...

Towards Corrective Deep Imitation Learning in Data Intensive Environments: Helping robots to learn faster by leveraging human knowledge

Interactive imitation learning refers to learning methods where a human teacher interacts with an agent during the learning process providing feedback to improve its behaviour. This type of learning may be preferable with respect to reinforcement learning techniques when dealing with real-world problems. This fact is especially true in the case...

Data efficient pick location learning from images for sequential tasks

Learning from demonstration is a technique where the robot learns directly from humans. It can be beneficial to learn from humans directly because humans can easily demonstrate complex behaviors without being experts in demonstrating required tasks. However, it can be challenging to gather large amounts of data from humans because humans often...

Uncertainty-based Interactive Machine Learning

Interactive machine learning describes a collection of methodologies in which a human user actively participates in a novice agent’s learning process, through providing corrective or evaluate feedback or demonstrative actions. A primary assumption in these methods is that user input is at worst nearoptimal, however a realistic set of...