MIT’s M-Blocks: A New Class Of Robot Cubes That Self Assemble

What if robots could reassemble themselves at will? The liquid metal cyborg in Terminator was terrifyingly useful. It could look like anyone, repair shotgun blasts, even turn its hand into a murderous icepick. And then of course, you've got Transformers, wherein alien robots morph from cars and trucks into giant humanoid fighting machines.

It isn't liquid metal nor is it extraterrestrial, but MIT's John Romanishin, Daniela Rus, and Kyle Gilpin think they’ve found a promising precursor to a similar technology.

By building simple, independent modules that can separate and recombine at will, you can design a robot of flexible functionality. Such modular robots have been around for a long time. Indeed, we’ve covered plentyin the past. But none are as simple as MIT's M-Blocks, and it’s that simplicity that’s got folks excited.

[youtube https://www.youtube.com/watch?v=6aZbJS6LZbs]

The first thing you’ll notice about M-Blocks is how they move. All locomotion is self-contained—there are no external moving parts. Each block contains a 20,000 RPM flywheel which imparts angular momentum to each cube. They can move across the floor, roll over each other, and even leap about like a Mexican jumping bean.

The result is a system of discrete components capable of joining together to form a shape and then breaking apart and reassembling into another shape.

M-Blocks are distinct from other modular robots because, instead of being in control throughout the assembly process, there are moments of chaos, where the blocks' location isn't precisely regulated. That would be a problem for self-assembling robots that require certain external components to match up perfectly to unite.

What makes this chaos acceptable in M-Blocks? Magnets. When they are close to one another, the magnets on the cubes’ edges passively align their poles and straighten the cube, allowing the face magnets to snap together.

Because there is no special orientation required—for connectors to meet up, for example—any side will do.

These edge magnets also allow the cubes to pivot around each other. Because the magnets are chamfered, the magnets touch when they pivot, and the bond is strengthened.

In the future, the team envisions equipping special blocks with more horsepower to pull weaker blocks along or battery packs for extra juice. Eventually, some blocks might carry cameras, lights, or grippers to maneuver objects or handle tools. At the current scale, the team thinks advanced versions of M-Blocks might be used to repair infrastructure, build and reconfigure scaffolding, or assemble furniture or heavy equipment.

But it’s taking the design smaller that really sparks the imagination.

Reduced to nanoscale, swarms of M-Blocks might become the voxels of self-assembling macro-bots. The smaller the block—the higher the robot’s voxel resolution. Or made compatible with human biology, they could be used to attack tumors or repair organs.

M-Blocks make such dreams ever so slightly more realistic because of their simplicity. The simpler the constituent parts, the cheaper and easier they are to make, control, and miniaturize. Complexity can arise in how they come together and in which configurations—not unlike cells in living things.

But of course, all that’s a long, uncertain way off, and technology has a way of surprising even our dearest held dreams. These early prototypes aren't autonomous, and they are only capable of forming shapes, not functioning tools or infrastructure.

For now, the team is focused on improving their design. They’re building 100 new M-Blocks for further experimentation. Whereas the current blocks are individually controlled by their handlers, the new blocks have enough processing power onboard to do some things autonomously. The team is also working on new software to control them.

Ideally, a human using M-Blocks would give a general instruction to form a particular shape, and the blocks would figure out how to do it on their own.

Romanishin told MIT news, “We want hundreds of cubes, scattered randomly across the floor, to be able to identify each other, coalesce, and autonomously transform into a chair, or a ladder, or a desk, on demand."

Image Credit: MIT News/YouTube

New MIT algorithms help robots combat uncertainty

In today’s installation of “Exactly how the robots will certainly someday eliminate all human beings,” we have a job from MIT taking care of robotic partnership. Allow’s state you set one robotic to do a specific job, and also one more to do something associated. Exactly how do you maintain them from entering each various other’s method? A more concept would certainly be a method to obtain the robots operating in the exact same area while integrating their activities in genuine time. That’s exactly what the MIT group is dealing with.

The research study handles a kind of robot automation called Decentralized Partly Observable Markov Choice Processes (Dec-POMDPs). These are mathematical designs that explain the method a multi-agent system acts– it’s not simply for robots, as any type of independent networked system would use. The issue the MIT scientists are looking for to fix is among uncertainty: The even more representatives in a system, the much more complicated as well as susceptible|susceptible so that complicated to failure it is. There’s constantly a specific quantity of mistake in the sensing unit information each robotic utilizes to remain on track, which amounts to make complicated activities tough to strategy.

Last summertime a various MIT group released a paper revealing Dec-POMDPs can be utilized to combine alreadying existing robot command systems to achieve jobs en masse. The mathematics made good sense, however just now have they really placed the strategy right into activity, as well as they doinged this with remote-control helicopters. It’s a restricted take on exactly how you would certainly handle an automatic drone distribution solution like the one Amazon.com has actually suggested.

The examination utilized to assess the precision of the Dec-POMDP algorithms included a variety of base terminals spread throughout a space. Close-by were bundle distribution places. The helicopters would certainly have to go across each various other’s courses to make all the “distribution,” so exactly how do you make that occur without creating an accident? Prior to the robots begin flying available, there’s an offline preparation stage where each representative draws up the approximate course– an academic method of achieving the job. From there, it depends on the charts.

The circumstance is destroyed down right into 2 charts by the algorithms. One produces a collection of prospective micro-actions, and also the various other stands for shifts in between macro-actions due to monitorings from all the networked representatives. The preparation formula runs different beginning states with so that appoints worths to all the micro-actions. The outcome is a chart of the possibility that a representative (robots, keep in mind) ought to carry out a specific activity at a specific time.

This procedure is duplicated for every activity up until all the drones have actually made it securely where they have to go. Preventing accidents is a fairly basic issue, however the exact same principal might be utilized to automatic much more complicated situations in the future. A complex situation like eliminating all human beings, for instance.

ExtremeTech| In today’s installation of “Exactly how the robots will certainly one day eliminate all human beings,” we have a job from MIT dealing with robotic partnership. Allow’s state you set one robotic to do a specific job, so that one more to do something associated. These are mathematical designs that explain the method a multi-agent system acts– it’s not simply for robots, as any kind of independent networked system would use.

In today’s installation of “Exactly how the robots will certainly one day eliminate all human beings,” we have a job from MIT dealing with robotic partnership. Allow’s state you set one robotic to do a specific job, as well as one more to do something associated. These are mathematical designs that explain the method a multi-agent system acts– it’s not simply for robots, as any kind of independent networked system would use. Prior to the robots begin flying available, there’s an offline preparation stage where each representative maps out the approximate course– an academic method of achieving the job.