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Robots are used worldwide in many industrial processes, and are getting better at helping humans every year. Machine learning algorithms are enhancing the capabilities of traditional robotics, and have become essential in making robots more adaptable to challenging situations.
Embodied machine learning seeks to emulate the ways in which humans process information. By using a wide variety of sensors on robotic hardware, researchers are able to help robots perceive, analyze, interact, and navigate through unpredictable physical environments. Mila researchers are tackling challenges such as better long-term planning for the use of robots in daily life, building representations of the world — including simultaneous localization and mapping — while creating better workflows to teach robotic agents new tasks.
Mila’s work also includes designing experimental machine learning algorithms to help robots perform better in industrial applications such as assembly and disassembly, meal preparation, and warehouse management.
DROID is an initiative that aims to address the scarcity of comprehensive datasets in robotics, enhancing the development of manipulation algorithms for real-world applications.
ConceptGraphs is a mapping system that builds 3D scene-graphs of objects and their relationships, enabling robots to perform complex navigation and object manipulation tasks.
AI can help us make robots more adaptable to unpredictable environments, which will lead to true robotics assistants in the real world.
Mila professors exploring the subject as part of their research.
Prof. Glen Berseth studies how machine learning can be used to train more adaptable robots that could help humanity meet its most pressing challenges.
