What You Will Learn

• The vision and rationale for general-purpose humanoid robots
• Hardware design metrics for human-like strength and dexterity
• Software, AI, and data strategies for achieving full autonomy
• Commercial deployment, manufacturing at scale (Bot.Q), and sales-as-work models
• Best practices for collecting real-world data and iterating across a robot fleet

Study Guide

Introduction: The New Era of General Purpose Robotics

Imagine a world where robots move among us,not as distant science fiction, but as everyday workers, helpers, and companions. This course is your gateway to understanding how Figure AI, through the bold vision of its CEO, is making that world a reality by building general purpose, human-like robots designed to transform industries and daily life alike.

In this comprehensive guide, you’ll journey from the foundations of humanoid robotics to the advanced strategies Figure AI is deploying to lead this emerging revolution. You’ll learn not only the technical and business aspects of building and deploying these robots, but also the mindset and practical execution behind one of today’s most ambitious technology ventures. Whether you’re a business leader, technologist, or simply curious about the future of work and automation, this course will equip you with deep insights and actionable understanding.

The Vision: Why Build General Purpose Robots?

At the heart of Figure AI is a vision that goes beyond mere automation. The company's CEO describes humanoid robotics as the foundation for what could be the largest business opportunity and product launch of all time.

The goal isn’t to create one-off solutions, but “synthetic humans” capable of performing the majority of tasks a real person can do,across both commercial and consumer environments. This means robots that can work on manufacturing lines, handle packages in warehouses, and potentially even help with laundry or dishes in homes.

Why is this vision so compelling? For one, global labor populations are declining, and the demand for reliable, scalable workforce solutions is rising. Robots that can fill these roles,without the constraints of fatigue, turnover, or skill gaps,unlock enormous value.

Example 1: A logistics company faces seasonal spikes in order volumes but struggles to hire enough workers. Deploying humanoid robots, they can scale up or down instantly, maintaining service quality and cost control.
Example 2: In healthcare, general purpose robots could eventually assist with patient lifting, room cleaning, and even basic caregiving, freeing up human staff for more complex, personal interactions.

Commercial Deployment: Robots at Work Today

Figure AI isn’t just theorizing about the future,they have humanoid robots “working every day in the commercial workforce.”

Their robots are already operating in environments like BMW’s automotive body shop, reliably moving sheet metal. This is not a closed test but real-world deployment, providing both immediate value and critical learning data for future improvement.

Example 1: At BMW, robots autonomously transport heavy, awkward pieces of sheet metal between workstations. This task requires precise handling, awareness of complex surroundings, and the ability to adapt to unexpected obstacles.
Example 2: In the logistics sector (with UPS as a reported partner), robots are tasked with picking, sorting, and moving diverse packages,each with different shapes, weights, and barcodes. Unlike a single-purpose robot arm, these humanoid machines generalize across endless variations.

Deploying robots commercially,rather than in labs,accelerates data collection, exposes real operational challenges, and builds customer trust.

Full Autonomy: The Core of the Strategy

Teleoperation (remote control) is not the endgame for Figure AI. The CEO is clear: “We don't want to be doing any remote control or teleoperation in the market.”

While remote operation is used during development for data gathering and testing, the company’s goal is to deploy robots at scale with no human in the loop. Full autonomy means the robots perceive their environment, make decisions, and act independently, unlocking truly scalable solutions.

Example 1: In a warehouse, a robot detects a new type of package it’s never seen. Instead of being manually guided, it uses its neural network to generalize, recognize the package, and determine how best to pick it up.
Example 2: In a hotel, an autonomous robot navigates crowded hallways to deliver towels,no remote operator required. It adapts in real-time to people, carts, and other obstacles.

Best Practice: To approach full autonomy, collect diverse, high-quality real-world data, and build feedback loops that allow robots to learn continuously from their environment.

Hardware: Built for Human-Like Capabilities

The hardware underpinning Figure AI’s robots is designed to mimic the range of human movement, strength, and dexterity.

Key design metrics include range of motion, torque, power output, payload capacity, and movement speed. The Figure 2 robot was initially built for capability, while Figure 3 achieves similar function at dramatically lower cost,a 93% reduction.

Example 1: In the BMW body shop, the robot’s arms and hands must have sufficient torque to lift heavy sheet metal, but also the finesse to avoid damaging delicate parts.
Example 2: For home use, robots may need to pick up soft laundry, open doors, and operate appliances,requiring both strength and a soft touch.

Best Practice: Design hardware for a wide range of use cases, and test extensively in environments that mirror real-world conditions. Cost optimization, as seen with the Figure 3, is crucial for scaling production and adoption.

Software and AI: The Critical Differentiators

“Much of all of this is at this point software.” The CEO emphasizes that, for most tasks, robots don’t need new hardware,just better software and more data.

Figure AI develops all AI and software in-house, believing this yields superior results compared to outsourcing or relying solely on open-source models. Their stack includes an open-source backbone (Gradio and Helix), but all data collection, model development, training, and inference are proprietary.

Example 1: When tasked with folding laundry, the robot’s neural network interprets visual data from cameras, identifies garment types, and learns folding techniques,all through software updates, not hardware changes.
Example 2: In logistics, the robot’s AI must generalize from millions of package examples to handle new shapes and barcodes, using deep learning and real-time sensor data.

Best Practice: Develop a robust, modular software architecture that supports rapid iteration and deployment of new skills. In-house data infrastructure accelerates learning and protects intellectual property.

Data as the Bottleneck: Why Data (Not GPUs) Matters Most

Contrary to much of the AI industry buzz, Figure AI identifies data,not computing power,as the primary bottleneck.

The company’s challenge is “a data problem and a robot problem.” Real-world data from deployed robots is the lifeblood for training and improving AI models. GPUs are necessary, but without enough diverse, high-quality data, even the best hardware can’t deliver meaningful advances in autonomy.

Example 1: For a robot to handle every type of package found in a logistics center, it must see and manipulate millions of real packages,not just simulated ones. Each new deployment expands the data set, improving the entire fleet.
Example 2: In a home, robots collect data as they encounter new layouts, furniture, and appliances, allowing the AI to generalize across households and environments.

Best Practice: Prioritize real-world deployments, even in limited pilots, to gather the data needed for AI improvement. Focus on diversity and scale of collected data, and build infrastructure to rapidly integrate new learnings into deployed robots.

Vertical Integration: Building the Full Stack

Figure AI’s strategy is to provide a fully vertically integrated solution,handling hardware, electronics, software, AI models, manufacturing, and service delivery in-house.

This approach means customers aren’t just buying a robot,they’re buying “work.” The robot arrives ready to perform tasks autonomously with simple speech commands, with all supporting systems and maintenance handled by Figure AI.

Example 1: In manufacturing, a factory can deploy Figure AI robots without needing to coordinate between multiple vendors for hardware, software, and integration support.
Example 2: For a logistics company, Figure AI provides the robots, the AI that enables package handling, the service infrastructure for repairs, and a single point of accountability.

Advantages: Faster innovation cycles, tighter quality control, seamless customer experience, and better protection of proprietary technology.
Potential Disadvantage: High operational complexity and risk if any single part of the stack underperforms.

Best Practice: Maintain close feedback loops between each layer of the stack, and foster cross-disciplinary teams to quickly solve problems and iterate on both hardware and software.

Use Cases: Manufacturing, Logistics, and Beyond

Figure AI’s robots address both commercial and consumer markets, with current focus on manufacturing and logistics.

Manufacturing Example: At BMW, robots operate in the body shop, moving sheet metal with precision. This environment is ideal for early deployment,repetitive, high-value tasks in a controlled setting.
Logistics Example: In partnership with a leading logistics company (reported as UPS), robots handle the complex task of picking, sorting, and moving a wide variety of packages. The diversity of package shapes, weights, and labels challenges the robot’s generalization abilities.

Beyond these, potential future applications include construction, retail, healthcare, hospitality, and household tasks like cleaning, laundry, and basic maintenance.

Best Practice: Start with high-value, repetitive tasks in structured environments to build confidence and gather data, then expand into more complex and dynamic settings as AI capabilities improve.

The Challenge of Manipulation: Why Logistics Is Hard

One of the toughest technical challenges is manipulation,especially in logistics, where every package is different.

Robots must grasp, orient, and move items of varying shapes, weights, and fragility. Unlike a fixed industrial robot that repeats the same motion, a general purpose robot must generalize its skills.

Example 1: A robot picks up a box with a glossy surface and a round tube with a barcode, needing to adjust grip force, orientation, and camera angle for each.
Example 2: In a consumer setting, the robot loads a dishwasher,handling glasses, plates, and utensils, each requiring unique manipulation.

This challenge can’t be solved by programming every possible scenario. Neural networks allow the robot to learn from millions of examples and adapt to new objects on the fly.

Best Practice: Use vision and tactile sensors to collect diverse manipulation data, and deploy robots in environments where they encounter a wide array of objects to accelerate learning.

Manufacturing at Scale: The Bot.Q Approach

Figure AI’s manufacturing philosophy treats robots more like consumer electronics than cars,enabling rapid, high-volume production.

Their proprietary manufacturing process, called Bot.Q, is designed for scalability. Each production line can reportedly build 12,000 robots per year, and Figure AI has a target to ship 100,000 units in four years.

Example 1: In a dedicated facility, modular robot components are assembled, tested, and packaged for deployment,mirroring the processes used in smartphone manufacturing.
Example 2: The company’s own Manufacturing Execution System (MES) tracks every step from raw material to finished robot, optimizing for speed and quality.

The CEO sees this as orders of magnitude easier than car manufacturing, due to fewer parts, less regulatory overhead, and more automation.

Best Practice: Build flexible production lines that can quickly adapt to design changes and demand spikes. Invest in digital MES systems for real-time monitoring and continuous improvement.

Customer Relationships: Learning from Real World Deployments

Early customers like BMW and (reportedly) UPS are not just revenue sources,they are key partners in refining and proving the robots’ real-world utility.

Deploying robots in demanding, operational environments generates priceless data and feedback. These relationships also validate Figure AI’s business case for “selling work” rather than just hardware.

Example 1: At BMW, engineers and operators provide feedback on robot reliability, safety, and integration within existing workflows.
Example 2: In logistics, the robots face unpredictable package types, requiring rapid iteration and direct collaboration with warehouse staff.

Best Practice: Treat pilot customers as co-development partners. Use their feedback to drive product and AI improvements, and share progress transparently to build trust and advocacy.

Financial Strategy: Capitalization and Growth

Figure AI is well-funded, with resources to support extensive R&D, manufacturing, and recruitment. They have enough capital to finance thousands of robots and maintain a top-tier team.

While an IPO is not imminent (the CEO prefers private operation for now), their strong balance sheet allows aggressive investment in both technology and go-to-market efforts.

Example 1: The company can purchase sufficient GPUs, sensors, and parts without worry of cash constraints.
Example 2: They can hire world-class AI scientists and robotics engineers, outcompeting smaller or less well-capitalized rivals.

Best Practice: Use financial strength to accelerate learning and deployment, not just to accumulate cash. Invest in talent, infrastructure, and partnerships that create long-term defensibility.

The Winner-Take-All Dynamic in Robotics

In the CEO’s view, humanoid robotics could be a “winner take all” market, where the company with the most capable, lowest-cost, and continuously learning robots dominates.

The advantages compound: a fleet of deployed robots generates more data, which improves AI, which makes the robots more useful and affordable, attracting more customers and creating a positive feedback loop.

Example 1: As Figure AI’s robots work in more factories and warehouses, they encounter new edge cases, making their neural networks smarter than competitors stuck in limited pilots.
Example 2: Lower costs from manufacturing scale allow Figure AI to price robots more competitively, making it harder for latecomers to catch up.

Best Practice: Focus on rapid deployment and data collection. Build a business model that reinvests learning into improving the product for all customers, not just one-off projects.

Sales Strategy: Selling “Work” Instead of Just Hardware or Software

Figure AI’s sales approach is to deliver “work”,robots that perform tasks autonomously,rather than selling just a machine or a software license.

This model differentiates them from companies that provide only hardware or AI algorithms. Customers pay for outcomes, not tools. This aligns incentives and simplifies adoption.

Example 1: A warehouse pays Figure AI for a certain volume of packages processed per day, not for the robot itself. Figure AI takes responsibility for uptime, maintenance, and software updates.
Example 2: In a future home setting, a subscription model could allow families to “hire” a robot to handle daily chores, with all service and upgrades included.

Implication: This model creates recurring revenue, deeper customer relationships, and ongoing opportunities to improve the product through real-world feedback.

Transparency and Execution: Building in Public

While Figure AI’s CEO prefers to focus on building rather than attending events, the company maintains high transparency through social media and digital channels.

This serves two purposes: attracting top talent and showing tangible progress to customers and investors. Transparency builds trust and creates momentum.

Example 1: Regular video updates showcase robots performing new tasks, providing evidence of progress and inviting feedback from the broader community.
Example 2: Open discussion of challenges and failures positions Figure AI as a learning organization, not just a marketing machine.

Best Practice: Use transparency as a tool for both recruiting and customer engagement. Document and share progress honestly, and take public feedback seriously.

Glossary of Key Concepts and Terms

To anchor your learning, here are critical terms you’ll encounter throughout the course:

• Humanoid Robotics: Robots designed to mimic the human body and its functions, enabling them to perform tasks in environments built for people.
• General Purpose Robots: Machines built to handle a wide variety of tasks, unlike specialized robots limited to a single function.
• Teleoperation: Remote control of a robot by a human operator,used for development but not intended for large-scale deployment at Figure AI.
• Autonomous Activities: Tasks performed by robots independently, without human intervention.
• Vertical Integration: Controlling every step of the product journey, from design to manufacturing to deployment and support.
• Manufacturing Execution System (MES): Software that tracks and optimizes every step of the manufacturing process.
• Neural Network: The AI model powering the robot’s perception, decision-making, and generalization abilities.
• Data Problem: The challenge of acquiring enough diverse, real-world data to train robust AI models.
• Winner-Take-All Market: A scenario where network effects and scale advantages allow one or a few companies to dominate the industry.
• Selling Work: A business model where customers pay for completed tasks or outcomes, not just equipment or software.

Tips and Best Practices for Applying These Insights

1. Start with Clear, Ambitious Goals
Dare to imagine big impact, but ground your vision in real-world deployments and measurable progress.

2. Prioritize Real-World Data
Get robots into operational environments as early as possible. Let data drive your AI and product development cycles.

3. Build the Full Stack
Where possible, own the hardware, software, and customer experience. Integration reduces friction and accelerates learning.

4. Sell Outcomes, Not Just Tools
Align your business model with customer value,deliver results, not just technology.

5. Practice Radical Transparency
Share both successes and failures. Use public documentation and engagement to attract top talent and feedback.

6. Embrace the Experience Curve
The more robots you build and deploy, the cheaper and better they get. Scale isn’t just about volume,it’s about learning and improvement.

Conclusion: The Road Ahead for General Purpose Robots

Building general purpose humanoid robots is one of the most ambitious and impactful undertakings of our era. Figure AI’s approach,rooted in bold vision, vertical integration, relentless execution, and data-driven improvement,offers a roadmap for entrepreneurs, engineers, and business leaders alike.

You’ve explored the why and how of creating robots that can perform meaningful work in the real world. The journey from lab prototype to commercial deployment is complex, but the rewards are immense. By applying the principles explored in this course,focusing on autonomy, data, integration, and customer outcomes,you’ll be better prepared to participate in, or even lead, the coming wave of robotics transformation.

Remember, it’s not just about building robots,it’s about building a better way to work, live, and create value. The skills, strategies, and mindset you’ve learned here are your toolkit for the future.

Frequently Asked Questions

This FAQ section is designed to provide clear, thorough answers to both common and advanced questions about Figure AI and the process of building general-purpose humanoid robots. It addresses everything from the company’s vision and technical strategy to practical deployment and real-world business implications, offering a helpful guide for business leaders, technical professionals, and anyone interested in the future of robotics.

What is the core vision behind Figure AI?

Figure AI's core vision is to build general-purpose humanoid robots capable of performing a wide range of human-like work in both commercial and consumer settings.
They aim to create robots that can operate autonomously and learn from their experiences, aspiring to launch what they believe will be the biggest business and product introduction ever. Their focus is on developing robots that are not only functional but can also adapt and improve within real-world environments.

Why did Figure AI decide to focus on humanoid robotics?

Figure AI’s founder was inspired by science fiction and saw a unique opportunity as technology matured in both hardware and software capabilities.
The humanoid form was chosen because it can interact with environments built for humans, making it versatile for a wide array of tasks. The founder views building a general-purpose humanoid robot as an “ultimate problem”,highly challenging but with the potential for significant global impact.

What are Figure AI's main target markets for their robots?

Figure AI is targeting both the commercial workforce and the consumer household.
In commercial sectors like manufacturing and logistics,which represent a significant share of global economic activity,robots can address labor shortages and enhance productivity. For consumers, the vision includes robots performing routine tasks such as laundry, dishwashing, and tidying up, freeing people from repetitive chores.

How does Figure AI approach the autonomy of their robots?

Figure AI aims for full autonomy in their robots without human intervention.
While they currently use teleoperation for data collection and testing, their primary objective is to achieve robots that can perform complex tasks independently in diverse real-world scenarios. This commitment to autonomy is intended to avoid limiting progress at a “local maximum,” ensuring the technology continues to evolve and improve.

How does Figure AI view the manufacturing process for humanoid robots?

Figure AI likens humanoid robot manufacturing to consumer electronics rather than car manufacturing.
They have designed their own manufacturing execution system and automated many processes, which allows for scalable production. This approach supports the goal of delivering significant volumes over time, with the confidence that their systems can adapt for mass deployment.

What kind of tasks are Figure AI's robots currently performing in commercial deployments?

Figure AI robots are actively working at BMW, moving sheet metal within the Body Shop for car chassis welding.
They are also starting deployments in the logistics sector, focusing on handling packages of various sizes and orienting them for scanning. These use cases require robots to generalize and adapt to unpredictable real-world variables, showcasing their flexibility and learning capabilities.

How is Figure AI approaching the development of AI models for their robots?

Figure AI develops their AI models in-house, controlling the entire stack.
While they use an open-source backbone for semantic intelligence, all data collection, model development, training, and testing are managed internally. This approach ensures that the AI is optimized specifically for humanoid robots and supports rapid iteration and improvement.

How does Figure AI plan to achieve scale and maintain a competitive advantage?

Figure AI believes that scale is achieved by deploying large numbers of useful robots in the field.
As production increases, the cost per unit of work drops due to experience and efficiency gains. Robots deployed in the market learn daily and share improvements across the fleet, leading to smarter and cheaper robots. This network effect could create a “winner take all” scenario, where the company with the largest, most capable fleet dominates.

What is the primary goal of Figure AI, according to the CEO?

The primary goal is to sell “work,” not just robots.
Figure AI wants their robots to autonomously perform tasks across different environments,manufacturing, logistics, and households,delivering tangible value rather than simply selling hardware or software licenses.

Where does the CEO see the two main market areas for humanoid robots?

The two main market areas are the commercial workforce and consumer households.
Commercial applications include tasks in manufacturing and logistics, while consumer applications focus on everyday household chores.

What is Figure AI's approach to teleoperation or remote control of their robots in commercial settings?

Figure AI avoids teleoperation for real-world tasks, aiming for full autonomy.
Teleoperation is used only for data collection and testing. Their strategy is to ensure robots can independently handle complex tasks without ongoing human oversight.

What is Bot.Q and what is its claimed installed capacity?

Bot.Q is Figure AI’s proprietary manufacturing process and facility.
Each production line at Bot.Q is reported to have an installed capacity of 12,000 robots per year, supporting rapid scaling.

Figure AI claims a path to ship a large number of robots in four years. How many and what is the claimed basis for this rapid scaling?

Figure AI projects the ability to ship 100,000 robots within four years.
This ambitious target is based on their manufacturing model, which draws from the high-throughput, scalable processes seen in consumer electronics, rather than the slower, more complex car manufacturing process.

What is one specific task Figure AI robots are currently performing at BMW?

At BMW, Figure AI robots are moving sheet metal back and forth in the Body Shop.
This task is critical for car body assembly and involves precise handling and coordination, demonstrating the robot’s ability to integrate with existing manufacturing workflows.

Figure AI has a second named customer in the logistics space. Who is this customer reported to be?

According to reports, the second customer is UPS.
Figure AI is piloting robots in logistics environments, addressing challenges such as package handling, sorting, and orientation for scanning.

Why is solving manipulation problems in logistics, like handling packages, particularly challenging for robots?

Every package is different,size, shape, weight, and orientation vary constantly.
This complexity means you can’t simply pre-program every scenario. A neural network must learn to generalize across countless unique instances, making the logistics use case one of the toughest for robotic manipulation.

Does Figure AI rely solely on open-source AI models or do they develop their own?

Figure AI uses an open-source backbone but develops all core AI components in-house.
They manage data collection, model training, and inference testing themselves, ensuring the AI is tailored for their robots and real-world applications.

What does the CEO believe is the main bottleneck for Figure AI’s development: GPUs, data, or robots?

The CEO identifies data and robots as the main bottlenecks, not GPUs or cash.
The challenge lies in collecting enough high-quality data and deploying enough robots to accelerate learning and improvement.

How does Figure AI’s sales strategy of “selling work” differ from traditional robotics companies?

Instead of selling robots as products or licensing software, Figure AI charges for the completion of actual tasks performed by their robots.
This “work as a service” approach aligns pricing with delivered value, similar to how cloud computing charges for usage rather than for hardware or software alone. It encourages ongoing improvement and ensures customers only pay for productive results.

Why has Figure AI chosen a vertically integrated approach, and what are the advantages and disadvantages?

By designing hardware, software, AI models, and manufacturing processes themselves, Figure AI controls every aspect of performance and quality.
Advantages include faster iteration, better optimization, and independence from third-party bottlenecks. Disadvantages can include higher upfront costs and complexity, but the payoff is greater flexibility and alignment with long-term goals.

How does Figure AI differentiate manufacturing humanoid robots from car manufacturing, and what does this mean for scaling production?

Figure AI views humanoid robot production as more similar to assembling consumer electronics than cars.
This means focusing on modularity, rapid assembly, and automation, which allows for higher throughput and easier scaling. It reduces the barriers to increasing production and supports quicker deployment to customers.

What factors could lead to a “winner take all” market in humanoid robotics, according to Figure AI’s CEO?

The company with the largest, most capable, and continuously learning robot fleet gains a compounding advantage.
As robots share improvements and lower costs through scale, it becomes hard for newcomers to catch up. The market could tip in favor of whoever leads in deployment, intelligence, and value per cost.

What does “full autonomy” mean for Figure AI robots?

Full autonomy means robots operate and make decisions independently in real-time, with no human in the loop.
For example, a robot in a warehouse can identify, pick up, and move a package without remote instructions, adapting to new obstacles or changes in the environment on its own.

Why is gathering high-quality data so critical for Figure AI’s progress?

Quality data fuels the AI’s ability to learn and generalize to new situations.
In robotics, real-world environments are unpredictable. The more diverse and accurate the data, the better the robot can handle edge cases,like picking up a fragile item or navigating a cluttered room. Poor data limits improvement, while rich data accelerates development.

What is meant by a “robot problem” in Figure AI’s context?

The “robot problem” refers to challenges in getting enough physical robots built, deployed, and operating reliably in the field.
Even with great AI, progress slows if there aren’t enough robots gathering data and learning from real-world tasks. Scaling manufacturing, ensuring durability, and managing logistics are all part of the robot problem.

How do commercial and consumer use cases differ for general-purpose robots?

Commercial robots work in structured environments like factories or warehouses, performing repetitive or physically demanding tasks.
Consumer robots must handle varied, less predictable tasks,such as folding laundry or cleaning,within homes. The commercial setting allows for more control and easier deployment, while consumer use requires higher adaptability and safety.

What safety standards and precautions are important for deploying humanoid robots in workplaces and homes?

Safety is built into both hardware and software, including sensors to detect humans, emergency stop functions, and compliant actuators that avoid excessive force.
Robots are tested extensively to prevent accidents. In factories, they may operate in fenced areas, while in homes, advanced perception and soft touch are prioritized to ensure safe interaction with people and pets.

How are AI models for humanoid robots trained to handle new or unexpected situations?

AI models are exposed to a broad range of scenarios during training, using real-world data and simulated environments.
For example, a warehouse robot may be trained to recognize and manipulate thousands of package types, learning to adapt when a new object appears. Continuous learning from deployed robots enables rapid improvement in handling rare events.

Can Figure AI robots work alongside humans safely and efficiently?

Yes, robots are designed to collaborate with human workers.
They use computer vision and proximity sensors to detect people and adjust their actions accordingly. For example, a robot might pause or reroute if a worker steps nearby. This enables safe shared workspaces and can boost productivity by taking over repetitive tasks.

What are common challenges businesses face when implementing humanoid robots?

Challenges include integrating robots with existing workflows, managing employee expectations, and ensuring data privacy and security.
Technical hurdles can involve customizing robots for specific tasks or environments, while organizational challenges may relate to training staff and optimizing processes to work with automation. Overcoming these requires strong planning, clear communication, and ongoing support.

How should businesses evaluate the cost versus return on investment (ROI) for deploying Figure AI robots?

Key factors include task efficiency, labor savings, error reduction, and potential for new capabilities.
For example, a logistics center might compare the cost of robotic labor to human wages, factoring in uptime, reliability, and reduced injury rates. The right metrics depend on the business’s specific goals, such as throughput or quality improvement.

What are some real-world examples of tasks general-purpose robots can perform today?

Examples include moving car parts in automotive manufacturing, handling packages in distribution centers, and performing repetitive household chores like laundry or dishwashing.
At BMW, robots move sheet metal, while in logistics, they sort and orient packages for scanning. In homes, early pilots may automate routine cleaning or organization tasks, freeing up human time for more valuable activities.

What does “generalisation” mean in the context of Figure AI’s robots?

Generalisation refers to the robot’s ability to handle new, unseen situations based on prior learning.
For instance, rather than being programmed for every possible package shape, the robot learns underlying patterns, allowing it to pick up and orient any box it encounters,even ones it has never seen before.

How can businesses integrate Figure AI robots into their existing operations?

Integration involves identifying tasks suitable for automation, setting up interfaces between robots and existing software systems, and training staff to work with the new technology.
Pilot projects can help refine processes before scaling up. Close collaboration between technical teams ensures smooth rollout and maximizes value from the robots.

What is the long-term vision for consumer-focused humanoid robots?

The goal is to create robots that can perform a wide range of household tasks, learning new skills over time.
This could include everything from tidying rooms and folding clothes to preparing simple meals. As robots become more capable and affordable, their role in homes is expected to expand significantly, making daily life easier and more efficient.

How do robots in the Figure AI fleet share knowledge and improvements?

Data and insights from one robot’s experiences are uploaded, aggregated, and distributed across the entire fleet.
If a robot learns a better way to handle a particular object or task, that learning is shared, making every robot smarter. This collective improvement accelerates progress and drives down costs.

What does “human in the loop” mean, and why does Figure AI avoid it?

“Human in the loop” means a person is actively involved in robot decision-making or operation.
Figure AI avoids this for deployed robots to ensure true autonomy and scalability. It’s used only for data collection or testing, not for everyday operation, which reduces labor requirements and increases reliability.

How does Figure AI address the challenge of robot dexterity and the “softness of touch”?

Robots are equipped with advanced sensors and actuators to control grip strength and movement precision.
This allows them to handle delicate objects without damage,crucial for tasks like picking up fragile packages or working alongside humans. Continuous learning and feedback further refine dexterity over time.

What ethical considerations are important in deploying general-purpose robots?

Key concerns include job displacement, data privacy, and ensuring safe, fair use of technology.
Figure AI focuses on augmenting human work, not replacing it entirely, and implements strict safeguards for human safety and data handling. Transparency and accountability are built into both design and deployment processes.

What kind of maintenance is required for Figure AI robots, and how is it managed?

Regular software updates, hardware inspections, and preventive maintenance are part of the management process.
Remote diagnostics and predictive analytics help anticipate issues before they cause downtime. Service agreements and in-field support teams ensure robots remain operational and productive for customers.

How scalable is Figure AI’s approach to manufacturing and deploying robots?

Their approach is designed for rapid scaling, leveraging automation and modular assembly lines similar to consumer electronics production.
With facilities like Bot.Q and a focus on standardization, Figure AI is positioned to increase output as demand grows, supporting large-scale deployments across industries.

What is the difference between an open-source AI model and a proprietary model in Figure AI’s context?

An open-source model is publicly available and can be used or modified by anyone, while a proprietary model is developed and owned by a company.
Figure AI uses open-source elements as a foundation but builds proprietary layers for data collection, training, and inference, ensuring their models are uniquely fit for humanoid robotics.

How does Figure AI ensure the security of its robots and the data they collect?

Robots are equipped with strong cybersecurity measures, including encrypted communication, secure firmware, and regular vulnerability assessments.
Strict access controls and compliance with data privacy regulations protect both customer data and operational information, minimizing risk of unauthorized access.

What are potential obstacles to widespread adoption of general-purpose humanoid robots?

Barriers include high initial costs, integration with legacy systems, regulatory compliance, and building public trust.
Technical challenges such as durability, adaptability, and safe operation in varied environments also persist. Overcoming these requires ongoing innovation, transparent practices, and demonstration of clear business value.

Can Figure AI robots be customized for industry-specific needs?

Yes, robots can be programmed and trained for specialized tasks in industries like automotive, logistics, and retail.
Modular hardware and adaptable software allow for customization, whether it’s handling unique products, integrating with warehouse systems, or supporting diverse workflows.

Are there regulatory challenges involved in deploying humanoid robots?

Yes, compliance with safety, labor, and privacy regulations is essential in every deployment region.
Figure AI works with industry groups and regulatory bodies to meet or exceed all requirements, ensuring safe, legal operation in factories, warehouses, and homes.

How might business models evolve as humanoid robots become more widespread?

Subscription or pay-per-task models are likely to become common, reducing upfront costs and aligning payment with value delivered.
This approach can open access to smaller businesses and drive faster adoption, as customers pay for outcomes rather than equipment alone.

How does Figure AI stay ahead in innovation and avoid being overtaken by competitors?

Continuous learning, rapid deployment, and fleet-wide data sharing help Figure AI lead in performance and efficiency.
Vertical integration enables fast iteration and optimization, while partnerships and open-source collaboration keep the company at the forefront of technology trends.