Although we are all familiar with the five senses humans use to perceive our surroundings, for robots, these senses look a little different. Robots are rapidly becoming more prevalent in our everyday lives which can take a plethora of forms, from industrial applications, to delivery drones to robotic waiters. No matter what the role, these robots require sensors of all sorts to see the environment around them. The combination of cameras, LiDAR and gas sensors, together play a vital role in informing the robot’s unique perspective.

Robots are used for a host of different research topics and applications, from studying human-robot interaction to collecting samples in inhospitable environments to developing new commercial applications of robotics and artificial intelligence. All of these different research goals may require unique capabilities or specifications, but there are some consistent factors for what makes an effective robot in a research context. First and foremost, a research robot needs to serve the research; a robot that is costly or resource-intensive to build, maintain, or deploy takes time, energy, and budget away from a project or lab’s primary goals.

Spot’s easy mobility, along with a host of other benefits, has made it a popular choice in academia and research institutions. The ready-out-of-the-box quadruped robot helps researchers focus on their objectives, explore tough problems, and experiment with the many ways robotics can positively impact people’s lives. Here are just a few ways that educational and research organizations are working with Spot today.

Artificial Intelligence (AI) is rapidly becoming a routine aspect of business operations. A recent survey from Deloitte found 34% of respondents had already begun implementing AI systems to support intelligent automation, while 52% plan to implement these systems in the next three years. But implementing and operationalizing AI is often easier said than done, especially without continuous, reliable data about your operations.

There is a disconnect in manufacturing.

On one hand, artificial intelligence (AI) and machine learning (ML) have made huge advancements and offer tremendous potential to save maintenance costs and increase uptime by identifying problems before they escalate. On the other hand, the relevant data these ML models need is often hard to gather and aggregate into disparate systems. Legacy assets plod along without realizing the full promise of digital transformation and Industry 4.0.

Atlas jogs up to the embanked yellow plywood panels, bouncing with ease from one to the next until the humanoid robot has made its way up to a box that sits at about waist level. Sensing the gap in front of it, Atlas pauses, plants its feet, and broad jumps across to a table on the other side. 

It sticks the landing. By all appearances, Atlas is cruising. But then, as the robot descends the three stairs back to the floor, a geyser of pink hydraulic fluid erupts from its left knee. The robot gets a few more staggered steps in as the oil gushes into the air, and then collapses in a heap. 

For the first time today, both Atlas robots have completed the complex obstacle course flawlessly. Or, almost flawlessly.

The first of the two robots ran up a series of banked plywood panels, broad jumped a gap, and ran up and down stairs in the course set up on the second floor of the Boston Dynamics headquarters. The second robot leapt onto a balance beam and followed the same steps in reverse, and then the first robot vaulted over the beam. Both landed two perfectly synchronized backflips, and the video team has captured every move. 

Spot is more than a mobile robot; it’s a mobile sensing platform that developers can customize with software, sensors, and other hardware payloads to achieve exactly what they need for specific applications. Whether it’s building the tools to explore other planets or to inspect worksites, the ability to easily build on Spot’s base functionality has huge real-world value. 

This flexibility also opens up opportunities for more creative applications. For example, Spot has a storied history with alcoholic beverages, from inspecting breweries to doing other activities with beer. But before all that, there was Margarita Spot: an internal project developing a Spot payload that blends the perfect margarita.

It’s been a little over a year since Spot graduated from our early adopter program and was released for sale. Our early adopters helped us answer the question, “Can robots handle the real world?” Our customers’ experience with Spot over the past year has answered a follow up question, “Where is Spot adding value?”