The five senses of robotics (Common sense robotics)

The Common sense robotics

Common Sense Robotics, an Israel-based startup developing AI and robotics tech to help online grocery retailers speed up fulfilment and delivery, has raised $20 million in Series A funding.

The round was led by Playground Global, with participation from previous investors Aleph VC and Eric Schmidt’s Innovation Endeavors. It brings the company’s total funding to $26 million.

‘The funds will be used to scale up CommonSense Robotics’ facility deployment rate, develop their next generation of robotics and AI, and expand global operations and sales,” says the startup.

Using what it describes as advanced robotics and AI, CommonSense Robotics claims to enable retailers — even relatively small ones — to offer one hour, on-demand grocery deliveries to consumers “at a profitable margin”. It does this by employing robots to power bespoke warehouses or micro-fulfilment centers that are small enough to be placed in urban areas rather than miles away on the outskirts of town.

The robots are designed to store products and bring the right ones to humans who then pack a customer’s order. More robots are then used to get the packaged order out to dispatch. This robot/AI and human combo promises to significantly reduce the cost of on-demand groceries, thus broadening the range of retailers that can compete with Amazon

 

Other Applications and projects  for Common Sense Robotics

today we are going to talk about the The common senses robotics. As we know healthy humans take for granted their Common senses. In order to mold metal into perceiving machines, it requires a significant amount of engineers and capital. Already, we have handed over many of our faculties to embedded devices in our cars, homes, workplaces, hospitals, and governments. Even automation skeptics unwillingly trust the smart gadgets in their pockets with their lives.

Robots & LIDAR (Light Imaging, Detection, And Ranging):

Last week, General Motors stepped up its autonomous car effort by augmenting its artificial intelligence unit, Cruise Automation, with greater perception capabilities through the acquisition of LIDAR (Light Imaging, Detection, And Ranging) technology company Strobe. Cruise was purchased with great fanfare last year by GM for a billion dollars.

Strobe’s unique value proposition is shrinking its optical arrays to the size of a microchip, thereby substantially reducing costs of a traditionally expensive sensor that is critical for autonomous vehicles measuring the distances of objects on the road.

Cruise CEO Kyle Vogt wrote last week on Medium that Strobe’s new chip-scale LIDAR technology will significantly enhance the capabilities of our self-driving cars.

But perhaps more importantly, by collapsing the entire sensor down to a single chip, we’ll reduce the cost of each LIDAR on our self-driving cars by 99%.

GM is not the first Detroit automaker aiming to reduce the costs of sensors on the road; last year Ford invested $150 million in Velodyne, the leading LIDAR company on the market.

Velodyne is best known for its rotation sensor that is often mistaken for a siren on top of the car.

In describing the transaction, Raj Nair, Ford’s Executive Vice President, Product Development and Chief Technical Officer, said

From the very beginning of our autonomous vehicle program, we saw LIDAR as a key enabler due to its sensing capabilities and how it complements radar and cameras. Ford has a long-standing relationship with Velodyne and our investment is a clear sign of our commitment to making autonomous vehicles available for consumers around the world.

As the race heats up for competing perception technologies, LIDAR startups is already a crowded field with eight other companies (below) competing to become the standard vision for autonomous driving.

Robots & Columbia Grasp Database:

Walking the halls of Columbia University’s engineering school last week, I visited a number of the robotic labs working on the next generation of sensing technology. Dr. Peter Allen, Professor of Computer Science, is the founder of the Columbia Grasp Database, whimsically called GraspIt!, that enables robots to better recognize and pickup everyday objects.

• GraspIt! provides an architecture to enable robotic grasp planning via shape completion.


• The open source GraspIt! database has over 440,000 3D representations of household articles from varying viewpoints, which are used to train its 3D convolution neural network (CNN).


• According to the Lab’s IEEE paper published earlier this year, the CNN is able to serve up “a 2.5 D point cloud” capture of a single point of view of each item, which then fills in the occluded regions of the scene, allowing grasps to be planned and executed on the completed object (see diagram below).


• As Dr. Allen demonstrated last week, the CNN is able to perform as successfully in live scenarios with a robots seeing an object for the first time, as it does in computer simulations.

Brain-Computer Interfaces (BCI) and electroencephalogram (EGG):

Taking a novel approach in utilizing their cloud-based data platform, Allen’s team now aims to help quadriplegics better navigate their world with assistive robots.

Typically, a quadriplegic is reliant on human aids to perform even the most basic functions like eating and drinking, however Brain-Computer Interfaces (BCI) offer the promise of independence with a robot.

Wearing a BCI helmet, Dr. Allen’s grad student was able to move a robot around the room by just looking at objects on screen.

The object on the screen triggers electroencephalogram (EGG) waves that are admitted to the robot which translates the signals into point cloud images on the database.

According to their research: 

• Noninvasive BCI’s, which are very desirable from a medical and therapeutic perspective, are only able to deliver noisy, low-bandwidth signals, making their use in complex tasks difficult.


• To this end, we present a shared control online grasp planning framework using an advanced EEG-based interface.


• This online planning framework allows the user to direct the planner towards grasps that reflect their intent for using the grasped object by successively selecting grasps that approach the desired approach direction of the hand.


• The planner divides the grasping task into phases, and generates images that reflect the choices that the planner can make at each phase. The EEG interface is used to recognize the user’s preference among a set of options presented by the planner.

 Robotic & Manipulation and Mobility Lab (ROAM):

While technologies like LIDAR and GraspIt! enable robots to better perceive the human world, in the basement of the SEAS Engineering Building at Columbia University, Dr. Matei Ciocarlie is developing an array of affordable tactile sensors for machines to touch & feel their environments.

Humans have very complex multi-modal systems that are built through trial & error knowledge gained since birth.

Dr. Ciocarlie ultimately aims to build a robotic gripper that has the capability of a human hand. Using light signals, Dr. Ciocarlie has demonstrated “sub-millimeter contact localization accuracy” of the mass object to determine the force applied to picking it up.

At Columbia’s Robotic Manipulation and Mobility Lab (ROAM), Ciocarlie is tackling one of the key challenges in robotic manipulation  by figuring out how you reduce the complexity of the problem without losing versatility.

While demonstrating a variety of new grippers and force sensors which are being deployed in such hostile environments as the International Space Station and a human’s cluttered home, the most immediately promising innovation is Ciocarlie’s therapeutic robotic hand (shown below).

 



According to Ciocarlie’s paper:

Fully wearable hand rehabilitation and assistive devices could extend training and improve quality of life for patients affected by hand impairments. However, such devices must deliver meaningful manipulation capabilities in a small and lightweight package In experiments with stroke survivors, we measured the force levels needed to overcome various levels of spasticity and open the hand for grasping using the first of these configurations, and qualitatively demonstrated the ability to execute fingertip grasps using the second. Our results support the feasibility of developing future wearable devices able to assist a range of manipulation tasks.

Dr. Hossam Haick and Cancer Detection Robots:

Across the ocean, Dr. Hossam Haick of Technion-Israel Institute of Technology has built an intelligent olfactory system that can diagnose cancer. Dr. Haick explains:

My college roommate had leukemia, and it made me want to see whether a sensor could be used for treatment. But then I realized early diagnosis could be as important as treatment itself.

Using an array of sensors composed of gold nanoparticles or carbon nanotube patients breath into a tube that detects cancer biomarkers through smell.

We send all the signals to a computer, and it will translate the odor into a signature that connects it to the disease we exposed to it.

says Dr. Haick. Last December

Haick’s AI reported an 86% accuracy in predicting cancers in more than 1,400 subjects in 17 countries.

The accuracy increased with use of its neural network in specific disease cases.

Haick’s machine could one day have better olfactory senses than canines, which have been proven to be able sniff out cancer.

 

When writing this post on robotic senses, I had several conversations with Alexa and I am always impressed with her auditory processing skills. It seems that the only area in which humans will exceed robots will be taste; however I am reminded of Dr. Hod Lipson’s food printer.

As I watched Lipson’s concept video of the machine squirting, layering, pasting and even cooking something that resembled Willie Wonka’s Everlasting Gobstopper, I sat back in his Creative Machines Lab realizing that Sci-Fi is no longer fiction.

Oliver Mitchell is the Founding Partner of Autonomy Ventures a New York based venture capital firm focused on seed stage investments in robotics. read more

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