A new robotics company is bringing characters into the physical world

Dynamic Creatures emerged from stealth on September 8, 2026 with a specific idea for physical AI: build mobile robotic characters that can move through real guest environments and interact with the person in front of them.

The Boston-based company is targeting entertainment and hospitality. Its first markets include theme parks, hotels and resorts, museums, cultural institutions, cruise ships and live venues.

Dynamic Creatures says it is already working with a large theme park and a retailer on guest-facing character experiences. The company has not named those customers, but the launch makes its direction clear. It wants advanced robotics to become part of the experience itself: a moving character that can notice what is happening around it and respond in real time.

Boston Dynamics gives the company a direct robotics platform connection

The hardware relationship is a major part of the launch.

Boston Dynamics confirms Dynamic Creatures as its official entertainment and hospitality partner. Dynamic Creatures can build character experiences on Boston Dynamics systems, while also creating fully custom robots for a specific brand, story or environment.

That gives the company two paths. A project can begin with an existing advanced robotics platform and add character design, behavior and interaction, or it can start with a custom machine built around the creative brief.

Dynamic Creatures was also founded by people with direct experience inside the robotics ecosystem. CEO Marc Theermann previously served as Chief Strategy Officer at Boston Dynamics and held senior roles at Google. CTO Farbod Farshidian previously led robotics research at the Robotics and AI Institute and worked on learning-based control for highly dynamic robots.

SnowJay is the intelligence layer behind the characters

The software platform is called SnowJay.

Dynamic Creatures describes SnowJay as an integrated robotics AI platform and the operating system at the heart of its characters. Its job is to coordinate movement, perception and behavior so the robot can operate as one connected physical system.

The platform combines real-time control with behavior orchestration, perception, character programming and the systems needed to turn those capabilities into a guest-facing experience.

That integration matters because an interactive character has to do several things at once. It needs to move through a space, observe the people and activity around it, select the right behavior, keep the motion fluid and deliver the response as part of the character.

Movement, perception and behavior run as one loop

SnowJay includes a real-time robot-control layer for executing behaviors and transitioning between skills.

Dynamic Creatures says the onboard platform includes policy and controller inference, behavior orchestration, skill execution, logging, monitoring and diagnostics. It also manages defined operating states such as controlled stopping and shutdown.

The interesting part is how those pieces connect. A character can perceive a person or event, choose an appropriate behavior, execute the movement and continue adapting as the interaction changes.

That turns the robot into a live system rather than a sequence of isolated AI features. Perception is connected to behavior. Behavior is connected to motion. Motion is connected back to the physical environment.

The human-robot interaction layer is designed around expression

Dynamic Creatures is also building SnowJay around human-robot interaction.

The company says its HRI layer brings together multimodal perception, conversation, memory, task orchestration, robot movement, animatronics, facial expressions, gaze, gestures, voice and human-facing interfaces.

It describes this as a character-focused form of vision-language-action interaction: human input is interpreted by the system and translated into physical output that can include movement, expression and gesture.

That is a useful way to understand the product. The AI is designed to connect language, perception and physical behavior inside the same character experience.

New physical skills can start with motion capture or animation

SnowJay also includes a learning and training pipeline for new robot behaviors.

Dynamic Creatures says teams can develop skills such as natural locomotion, expressive gestures and recovery behaviors, with training inputs that can come from motion capture or animation.

The platform also supports workflows where animations created in tools such as Maya can be brought into SnowJay. That connects a familiar character-animation process with a physical robot-control stack.

For entertainment teams, that creates an interesting bridge between creative direction and robotics engineering. A movement can begin as a designed performance, then become part of a robot behavior that SnowJay can coordinate with perception and interaction.

The first target is places where people already expect to meet characters

Theme parks and hospitality venues are a natural fit for the launch because interaction is already part of the experience.

Dynamic Creatures says its initial market includes theme parks, location-based entertainment, hotels, resorts, museums, cultural institutions, cruise ships and live venues. Future applications may also include luxury retail and automotive brand-experience centers.

The company says characters built on existing robotics platforms can be deployed in months, and it is offering leasing models for platform-based characters. That gives operators a route to introduce a robotic character without starting every project as a completely custom hardware program.

Dynamic Creatures is packaging mobility, AI, character design and interaction into one deployment model for venues that want a physical character people can encounter in the real world.

The Upgrade Feeling

Dynamic Creatures is a good example of where physical AI is heading next.

The robot is becoming more than a machine that moves. The software stack is starting to coordinate perception, language, memory, behavior, expression and motion around one live interaction.

SnowJay gives that idea a concrete architecture. Control runs on the robot. Skills can be trained and orchestrated. Human input can flow into an HRI layer. Character design shapes the final response. Boston Dynamics provides a proven robotics connection, while Dynamic Creatures builds the experience layer around it.

For theme parks, hotels and other guest-facing spaces, that could create a new kind of encounter: a character that can move through the same environment as the visitor, notice the moment and respond as part of the experience.

That is the upgrade here. Physical AI is starting to feel less like a demo and more like a character you can actually meet.

Atlas Is Moving From a Research Robot Into an Industrial Platform

Atlas spent more than a decade as a robotics research platform.

Boston Dynamics used earlier generations to study balance, locomotion, manipulation, perception and whole-body control.

That changed in 2024 when the company introduced a fully electric Atlas designed around future commercial work.

The next change arrived in January 2026.

Boston Dynamics unveiled the product version of Atlas at CES and said manufacturing would begin immediately at its Boston headquarters.

That is a different milestone from another capability demonstration.

A product robot has to be manufactured repeatedly.

It has to be serviced.

Integrated into workflows.

Managed as part of a fleet.

Connected to factory systems.

Updated with new skills.

Operated across shifts.

Boston Dynamics now describes Atlas as an enterprise humanoid for material handling and industrial automation.

The robot is still a robotics platform.

But the platform is now being shaped around deployment rather than research alone.

The Electric Redesign Started the Commercial Path

Boston Dynamics retired the hydraulic Atlas research platform in April 2024 and introduced a new all-electric robot.

The electric design was not simply a new power source.

It became the base for a commercial architecture.

Boston Dynamics said the new system was designed for real-world applications and would be developed with early customers beginning with Hyundai.

That created a direct path from prototype to customer environment.

Instead of designing every behavior only around a laboratory demonstration, the team could measure progress against work that exists inside a factory.

That is how part sequencing became important.

The task looks simple from a distance.

Pick a specific automotive component from one container.

Carry it.

Place it into another rack or dolly in the required sequence.

But the robot has to perceive the correct part, navigate around fixtures, choose a grasp, maintain balance, move the object and complete the placement.

A commercial application turns many research capabilities into one repeatable workflow.

Part Sequencing Became the First Industrial Application

Boston Dynamics chose automotive part sequencing as Atlas’ first major industrial application.

In a mixed-model vehicle plant, parts arrive from suppliers in containers.

Those components need to be reorganized into the exact sequence required by the assembly line.

Atlas has been trained to move parts such as engine covers from supplier containers into sequencing dollies.

Boston Dynamics uses the task because it combines several capabilities in one workflow.

Vision identifies bins and fixtures.

Manipulation selects and grasps the component.

Whole-body control keeps the robot balanced while reaching and carrying.

Navigation moves the robot through the workspace.

State estimation tracks both the robot and the object.

The sequencing task therefore becomes a practical integration test.

One successful pick is useful.

A production workflow needs the complete loop to repeat across many parts and changing conditions.

That is the difference between demonstrating a behavior and building an industrial application.

The 2025 Hyundai Pilot Put Atlas Into a Customer Factory

Boston Dynamics moved the electric Atlas into Hyundai Motor Group Metaplant America in Georgia for field testing in 2025.

Hyundai describes the pilot as part of the robot’s path toward commercialization.

Atlas repeatedly performed sequencing tasks involving automotive parts and racks.

Boston Dynamics says the deployment helped the team test application readiness outside the lab.

That matters because a customer factory has its own geometry, fixtures, lighting, schedules and operating systems.

The robot has to work inside that environment rather than a workspace designed only for robotics research.

Field testing also produces a different kind of engineering feedback.

The team can see which parts of perception need refinement.

Which grasps appear repeatedly.

How the robot interacts with real containers.

How operators communicate work.

How maintenance fits around production.

The factory becomes part of the development process.

The Product Version Is Built Around Repeatable Manufacturing

A product robot also has to be manufacturable.

Boston Dynamics says the 2026 Atlas product version reduces the number of unique parts and uses components designed for compatibility with automotive supply chains.

That is a product-engineering decision.

Research hardware can evolve quickly between builds.

Production hardware needs a more controlled bill of materials.

Parts have to be sourced.

Assemblies need repeatable processes.

Quality checks need stable specifications.

Replacement components need to be available.

Hyundai Motor Group brings another layer through its manufacturing network.

Hyundai says it plans to use its mass-production capabilities to support the expansion of Atlas production and deployment.

The robot therefore sits inside a wider industrial system.

Boston Dynamics develops the humanoid.

Hyundai contributes manufacturing scale and customer environments.

The product architecture has to work across both.

Atlas Has 56 Degrees of Freedom and Continuous Joint Rotation

The product version of Atlas uses 56 degrees of freedom.

Boston Dynamics also describes its joint range as continuous.

That gives the robot movement options beyond directly copying human anatomy.

A humanoid shape helps Atlas work in spaces designed around people.

But the robot does not have to reproduce every human mechanical limitation.

The joints can rotate through ranges selected for industrial manipulation.

Boston Dynamics has shown Atlas turning its body while carrying objects and using orientations that let the robot approach a task from different directions.

This matters inside a factory.

A rack may be behind the robot.

A container may be low.

A part may require a different grasp angle.

The robot’s morphology gives the planner more ways to solve the same physical task.

The product specification therefore connects mechanical design directly to application flexibility.

The Product Hardware Is Sized Around Industrial Work

Boston Dynamics lists Atlas at 1.9 meters tall and 90 kilograms.

Its current specification gives an instantaneous payload capacity of 50 kilograms, a sustained capacity of 30 kilograms and a one-handed capacity of 20 kilograms.

Reach is listed at 2.3 meters.

Those numbers define the physical envelope of the robot.

They tell an application engineer what kinds of bins, shelves, parts and workstations can be considered.

The robot also includes tactile sensing in its fingers and palm together with a 360-degree camera view.

That connects physical manipulation with perception.

The hands interact with the object.

The vision system models the surrounding workspace.

The body provides the reach and payload.

An industrial platform needs all three layers to be specified because the task is defined by the complete mechanical system, not one actuator.

Autonomous Battery Swapping Turns Power Into a Workflow

Battery operation becomes part of deployment when a robot is expected to work across shifts.

Boston Dynamics lists four hours of typical battery life and two hours under heavy lifting.

Atlas can autonomously navigate to a charging station and replace its own battery.

The current specification lists an autonomous battery-swap time of about three minutes and a charge time of 1.5 hours.

That changes the power workflow.

An operator does not have to manually open the robot and replace a pack every time energy runs low.

Battery replacement becomes another autonomous task.

The robot can pause its assigned work, move to the station, exchange the battery and return.

That is a product feature rather than a locomotion feature.

It exists because industrial operation includes energy management, shift scheduling and fleet availability.

The robot has to manage its own supporting infrastructure as part of the work cycle.

Serviceability Becomes a First-Class Design Requirement

Commercial robots also need a maintenance model.

Boston Dynamics lists Atlas components as modular and field replaceable.

The company says limbs can be replaced in the field and plans customer self-repair certification paths.

That changes the way the hardware is designed.

A research robot can return to the engineering team for extensive work.

A deployed robot needs maintenance that fits the customer’s operating environment.

Components need defined replacement procedures.

Technicians need access.

System monitoring needs to identify what requires service.

Replacement parts need known interfaces.

Boston Dynamics also gives Atlas an IP67 rating and an operating-temperature range from minus 20 to 40 degrees Celsius.

Those specifications describe the environment the product is designed to operate within.

The product version is therefore defined not only by what it can do when everything is running.

It is also defined by how it is maintained between tasks.

Safety Systems Are Integrated Into the Product Architecture

Atlas is designed for workspaces where people may also be present.

Boston Dynamics lists human detection and fenceless guarding in its current product specification.

The company describes an onboard safety system that detects people and vehicles around the robot.

If a person enters a defined nearby area, the robot can pause and wait.

Padding and reduced pinch-point exposure are also part of the current product design.

These are product-level systems because deployment depends on the complete operating environment.

The autonomous behavior decides where the robot wants to move.

The safety layer monitors the surrounding workspace.

The factory defines the broader process around the robot.

That creates several levels of control working together.

The robot is not only a machine that can walk and manipulate.

It is a machine being designed to operate as part of an industrial workplace.

Orbit Connects Atlas to Enterprise Systems

A factory robot also needs software above the robot itself.

Boston Dynamics uses Orbit as its fleet and enterprise-management layer.

Orbit can connect robotics workflows to Manufacturing Execution Systems, Warehouse Management Systems and other systems of record.

That link is important.

A factory does not assign work only through a person standing next to the robot.

Orders already exist inside digital systems.

Inventory has identifiers.

Production has schedules.

Parts can be tracked through barcodes and RFID.

Boston Dynamics lists barcode scanning and RFID as Atlas workflow integrations.

Orbit provides the management layer around those operations.

It can assign work, monitor performance and connect the robot fleet to the wider enterprise process.

That turns Atlas from an isolated autonomous machine into one endpoint inside a software-defined factory workflow.

One Learned Skill Can Be Distributed Across a Fleet

Boston Dynamics also treats learned robot behavior as a fleet asset.

The company says that when one Atlas learns a new skill, that task can be deployed across the wider Atlas fleet.

That changes the economics of training.

The physical work happens locally.

The learned behavior can become reusable software.

A robot can be trained for a sequencing operation.

Once the behavior is validated, the same capability can be delivered to other compatible Atlas systems.

The exact deployment still depends on the application, environment and integration.

But the model of improvement is no longer one robot at a time.

The fleet can share behavior updates.

That makes Atlas closer to an enterprise software platform.

Hardware performs the task.

Software defines the learned capability.

Fleet management distributes and monitors it.

This is one of the clearest ways the product version moves beyond the identity of one humanoid robot.

Reinforcement Learning Is Moving From Research Into Product Skills

Atlas behavior development now uses reinforcement learning, teleoperation data and learned behavior models as part of the product pipeline.

Boston Dynamics and the Robotics & AI Institute formed a joint reinforcement-learning program in 2025 for the electric Atlas.

Boston Dynamics also describes using reinforcement learning in simulation and from teleoperated demonstrations for factory behaviors.

The company has applied these methods to walking, manipulation, carrying and full-body movement.

The important change is where those methods end up.

They are not only research outputs.

They feed skills intended for industrial applications.

A policy trained in simulation can become part of a sequencing behavior.

A teleoperated demonstration can provide data for manipulation.

A whole-body controller can support lifting.

The research pipeline becomes a production-skill pipeline.

That is another layer required for a humanoid platform that is expected to learn new physical tasks over time.

Google DeepMind Adds Foundation Models to the Atlas Roadmap

Boston Dynamics and Google DeepMind announced a new AI partnership at CES 2026.

The two teams plan to combine Gemini Robotics foundation models with the new Atlas fleet.

The stated focus is industrial work, beginning with manufacturing.

This adds another software layer to the product roadmap.

Boston Dynamics already has locomotion, whole-body control, manipulation, perception and application-specific policies.

Foundation models can add broader reasoning and generalization capabilities above those systems.

The partnership is research work, so the exact production behaviors will develop over time.

But the architecture is clear.

Atlas is not being designed around one fixed set of hard-coded tasks.

The robot platform is expected to receive new behaviors through learning systems and foundation models.

That gives the product a software roadmap alongside its hardware roadmap.

Hyundai Is Building a Training and Validation Pipeline Around Atlas

Hyundai is also building infrastructure around the robot.

Its Robotics Metaplant Application Center, or RMAC, opened in the United States in 2026.

Hyundai describes RMAC as a site for training manufacturing AI robots, collecting real-world data, testing and verification before production deployment.

The Group says Atlas robots trained there are planned to begin sequencing work at Hyundai Motor Group Metaplant America from 2028, with more complex manufacturing operations targeted later.

Those dates are Hyundai’s roadmap.

They show the structure behind the rollout.

Train.

Validate.

Deploy.

Collect operational data.

Retrain.

Expand the task set.

The robot is only one piece.

The training center, factory, software systems and fleet-management process form the wider industrial platform around it.

The 2026 Product Version Has Already Moved Into Public Deployment

Atlas also made a public appearance at the 2026 FIFA World Cup.

Hyundai used the product robot during a live Round of 16 match environment in July.

Atlas performed football-inspired movements and delivered the ceremonial match ball.

Boston Dynamics says the same reinforcement-learning and whole-body-control methods used for that public performance are related to how the team develops industrial robot behaviors.

The event itself is not a factory task.

What matters is the deployment process.

The robot had to operate outside the lab.

It had to execute a defined sequence in a live environment.

The team tested the behavior in real-world conditions before the event.

Boston Dynamics describes that approach as part of the transition from prototype demonstrations toward production systems that need repeatable behavior.

Public deployment becomes another validation environment for the product architecture.

Atlas Is Becoming an Industrial Platform, Not Just a Humanoid Robot

The change in Atlas becomes clearer when all of these layers are placed together.

The electric robot provides the body.

Fifty-six degrees of freedom provide movement.

Tactile sensing and 360-degree vision provide physical awareness.

Autonomous battery swapping manages energy.

Modular field-replaceable components create a service model.

Safety systems support shared workplaces.

Orbit connects the fleet to MES, WMS, barcode and RFID workflows.

Learned behaviors can move across multiple robots.

Reinforcement learning and behavior models create new physical skills.

Google DeepMind adds foundation-model research.

Hyundai provides customer factories, RMAC training infrastructure and a deployment roadmap.

Boston Dynamics is manufacturing the product version now.

That is a much larger system than one humanoid completing one task.

Atlas is becoming a hardware platform, software platform and fleet platform at the same time.

The research robot proved what the body could learn.

The industrial platform has to make those capabilities deployable, maintainable and repeatable.

That is the upgrade.