MIT’s technique enhances robotic movement, AI enables robotic movement to seem more human-like

In a recent news article, MIT researchers have introduced a new and clever method to make it easier for robots to plan their movements when they need to interact closely with objects. This could bring a big change in how robots handle difficult tasks and make their motions seem more like how humans move.

This new approach uses a smart computer technique called 'smoothing'. This technique helps to shrink many instances where the robot touches things into a smaller group of choices. This way, even basic computer programs can quickly figure out good ways for robots to move things around effectively.

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📝 This blog discusses how AI is improving robots' manipulation abilities by enabling them to reason about moving objects using their entire bodies, not just...

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The power of ‘smoothing’ in robot planning

The conventional challenge of contact-rich movement planning arises from the complexity of accounting for countless potential contact points between the robot's appendages and an object. This leads to an overwhelming number of contact events to consider during planning, rendering the process unmanageable.

However, MIT's novel technique addresses this limitation by employing smoothing, effectively simplifying decision-making. By using smoothing, the researchers effectively refine a lot of minute adjustments into a handful of pivotal decisions, considerably reducing computational complexity.

They achieved this by integrating their physics-based model with an algorithm capable of efficiently searching through possible decisions. This powerful combination led to a remarkable reduction in computation time, with tests demonstrating planning times of around a minute on a standard laptop.

Rather than thinking about this as a black-box system, if we can leverage the structure of these kinds of robotic systems using models, there is an opportunity to accelerate the whole procedure of trying to make these decisions and come up with contact-rich plans

says H.J. Terry Suh, an electrical engineering and computer science (EECS) graduate student and co-lead author of a paper on this technique.

Benefits for factories & space missions

The technique's efficacy was proven through simulations and real-world experiments involving robotic arms performing tasks like manipulating objects to specific configurations, opening doors, and picking up items. Importantly, their model-based approach matched the performance of reinforcement learning but within a fraction of the time.

This innovation signifies a departure from the prevailing notion that only reinforcement learning could handle intricate tasks with nimble robotic hands. Despite its strides, the approach does have limitations. It's not suitable for highly dynamic motions like rapid object falls.

Yet, the researchers have a roadmap to refine the technique to encompass such scenarios. The implications of MIT's breakthrough are substantial. It could usher in a new era of mobile robots in factories that manipulate objects using their entire structures, potentially reducing energy consumption and costs.

Furthermore, this technique holds promise for space exploration missions, where onboard robots could rapidly adapt to alien environments. By harnessing the structured nature of robotic systems, MIT's research showcases the potential to expedite decision-making processes and formulate contact-rich plans more efficiently.