Generalizable Robotic Imitation Learning

Interactive Learning and Inductive Bias

Doctoral thesis (2024)

Authors

Rodrigo Pérez-Dattari Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering

Research Group

Learning & Autonomous Control (Mechanical, Maritime and Materials Engineering) (TU Delft)

Neural Networks Imitation Learning State Representation Learning Data-driven control Human-In-the-Loop Dynamical Systems Movement Primitives Inductive Bias

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Published Date

2024

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Cognitive Robotics

Research Group

Learning & Autonomous Control

Abstract

Robots have the potential to assume tasks across various real-world scenarios. To achieve this, we require adaptable and reactive robots that can robustly deal with products and environments that present variability. For example, in the agro-food sector, each tomato plant inside a greenhouse is unique; hence, different robotic motions are required when interacting with different plants. Unfortunately, due to their simplicity, most robotic solutions currently employed are rigid and rely on hand-crafted rules. Such solutions perform well in controlled and repetitive environments; however, they fall short when these conditions are not met. As a consequence, a large family of problems remains unsolved.....

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