AI News, Robotic experience companionship in music listening and video watching

Robotic experience companionship in music listening and video watching

We propose the notion of robotic experience companionship (REC): a person’s sense of sharing an experience with a robot.

Study I (\(n=67\)), examining music listening companionship, finds that the robot’s dance-like response to music causes participants to feel that the robot is co-listening with them, and increases their liking of songs.

Study II (\(n=91\)), examining video watching companionship supports these findings, demonstrating that social video viewers enjoy the experience more with the robot present, while habitually solitary viewers do not.

IAM Robotics Takes on Automated Warehouse Picking

There’s a small but growing handful of robotics companies trying to make it in the warehouse market with systems that work with humans on order fulfillment.

Generally, we’re talking about clever wheeled platforms that can autonomously deliver goods from one place to another, while humans continue do the most challenging part: picking items off of shelves.

Founded in 2012, they’ve developed anautonomous mobile picking robots calledSwift that consists of awheeled base anda Fanuc arm with a 15-lb payload and suction gripper that can reach 18 inches back into shelves.A height-adjustable carriage can access shelves between 3 and 85 inches, and an autonomously swappable tote carries up to 50 pounds of stuff.

According to the company, the robot canautonomously navigate around warehouses and is“capable of picking at human-level speeds” of 200 picks per hour.

As an example, he said that the robot,instead of dynamically calculating the best way to pick every item that it runs across,queries a database that consists of items that have already been scanned in 3D, modeled, and analyzed to figure out the best possible grasping strategies.

IAM Roboticsis currently conducting apilot project withRochester Drug Cooperative, one of the largest healthcare distributors in the United States.RDC is testings the Swift robots along with inventory tracking technology and fleet management software also developed by the IAM.

IEEE Spectrum: IAM Robotics is working on autonomously picking items off of shelves, which is something that most warehouse fulfillment companies aren’t trying to do yet, because it’s a really hard problem.

So we were doing things autonomously like finding objects on a table, picking them up, moving them around…we tried to do really challenging things like changing a tire, assembling parts, all kinds of stuff.

We got quite good at just general object “find it, pick it up and move it.” When we did that, we started looking for low-hanging fruit in industry, and we were led on a natural trajectory to picking in warehouses.

The products have to back one another up, because the way we’re picking them, we’re pushing on them from the front, and if you have light products that don’t have anything behind them, they’re going to tip over.

The majority of the work is done manually on the put away, where people are going to go on to make things a little bit more organized, but you’re saving five times what you spend in addition to your current workforce by automating the picking side.

I can’t disclose too much since that’s kind of like our secret sauce: the perception pipeline that we’ve built on top of off-the-shelf color cameras and depth sensors.

What we’ve done is we’ve layered on what we call our rapid vision algorithms that process the data extremely fast, extracts the information that we need to know know about the pick, and matches that that has already been visually trained for the system.

[The visual training comes from] taking our rapid vision system and packaging it into a standalone little photo studio scanner that we call Flash.

It’s just a standalone miniature photo booth, and we walk up and down each aisle one time, barcode scan each product, and put a sample of the product in the scanner.

So to a certain extent I think we’re going to see this granularity and differentiation in grippers and arms and we’re going to need to be able to support all of those to fill all the niches.

Inside Amazon’s Warehouse, Human-Robot Symbiosis

Trenton, New Jersey, isn’t the industrial powerhouse it once was, even if the slogan “Trenton Makes, the World Takes,” first installed in 1935, still stands in 10-foot-tall letters across a bridge that spans the Delaware River to Pennsylvania.

Amazon’s fulfillment center, located in the township of Robbinsville, is a dizzying hive of activity, with humans and machines working in carefully coördinated harmony.

Besides showing the incredible efficiencies of Amazon’s operations, the factory hints at how, over the coming decades, technology may start to assist human workers with many simple manual tasks.

In a carefully choreographed dance, these robots either rearrange the shelves in neatly packed rows, or bring them over to human workers, who stack them with new products or retrieve goods for packaging.

In recent years, however, thanks to better computer chips, algorithms, sensors, and actuators, robots have become cheaper, safer, and better able to learn new tasks quickly.

However, robots are still incapable of tasks that require fine manipulation or improvisation, so it is useful to devise ways for robots to collaborate with humans more effectively.

“It’s a natural outgrowth of efforts to harness cheap computing power to make robots more collaborative,” says Wily Shih, a professor at Harvard Business School who studies manufacturing.

Shih says the big hope is that robots will become “easier to drop into factory and distribution settings, and easier to integrate with existing manual processes and workers.” While Amazon’s warehouse is designed around its robots, some companies hope to develop robots capable of working in regular warehouses.


Like all of today’s leading robots, Swift is fully compliant with the ANSI B56.5 safety standards.

Traditional robots typically utilize lasers to detect obstacles, which have a very limited vertical field of view.

In contrast, Swift can detect nearly any obstacle within a 60 degree field of view both vertically and horizontally.

In contrast, Swift can detect nearly any obstacle within a 60 degree field of view both vertically and horizontally.

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