MIT engineers devise a recipe for improving any autonomous robotic system

Fri, 24 Jun 2022 06:12:01 GMT
Space Daily

Boston MA (SPX) Jun 22, 2022 Autonomous robots have come a long way since the fastidious Roomba. In...

Each of these robotic systems is a product of an ad hoc design process specific to that particular system.

In designing an autonomous robot, engineers must run countless trial-and-error simulations, often informed by intuition.

In some respects, designing an autonomous robot today is like baking a cake from scratch, with no recipe or prepared mix to ensure a successful outcome.

The team has devised an optimization code that can be applied to simulations of virtually any autonomous robotic system and can be used to automatically identify how and where to tweak a system to improve a robot's performance.

The team showed that the tool was able to quickly improve the performance of two very different autonomous systems: one in which a robot navigated a path between two obstacles, and another in which a pair of robots worked together to move a heavy box.

The researchers hope the new general-purpose optimizer can help to speed up the development of a wide range of autonomous systems, from walking robots and self-driving vehicles, to soft and dexterous robots, and teams of collaborative robots.

Normally, a roboticist optimizes an autonomous system by first developing a simulation of the system and its many interacting subsystems, such as its planning, control, perception, and hardware components.

The researchers developed an optimization framework, or a computer code, that can automatically find tweaks that can be made to an existing autonomous system to achieve a desired outcome.

Dawson and Fan built on recent advances in autodiff programming to develop a general-purpose optimization tool for autonomous robotic systems.

Building better robots The team tested their new tool on two separate autonomous robotic systems, and showed that the tool quickly improved each system's performance in laboratory experiments, compared with conventional optimization methods.

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