Humanoid Robot Scorecard – 25x Better in 10 Years?

The scorecard considers functional capabilities, ethics, user customization, and socio-cultural impact, incorporating forward-looking issues and drawing on comparisons to the impact of Moore’s Law for computing, which suggests exponential growth in various capabilities over time.

The “Score” is a relative measure of ability in relation to current human ability. Gradations of each score measured relative to other robots.

Sub-human – (Scores 1-3 – relative to alternatives) – Here Robots are a novelty and less likely to have broad commercial impact.

Human-level – (Scores 4-6 – relative to alternatives) – Robots have comparable abilities in all major human physical functions to the point where a robot is preferable to a human. At this point economics is the driver of growth.

Super-human – (Scores 7-9 – relative to alternatives) – Robot capability exceeds human capability in such a way that it is un-arguable. At this point utility is the driver of growth.

God-like – (Score 10 – relative to alternatives) – Robot capability so far exceeds human capability they appear to be God-like. At this point fear is an inhibitor of growth.

Exponential Growth Insight (Moore’s Law of Robotics):

As seen with many other technologies, particularly those that humanoid robotics are dependent upon, we can expect to follow an exponential curve of development in both hardware and AI capability.

Initially limited by cost and capability, over time robots will:

  • Reach human-like capabilities in enough aspects to gain momentum

  • Decrease in cost through mass production and innovation, making high-level robots accessible.

  • Shift from mere tools to active partners in personal growth and societal advancement.

By recognizing patterns across multiple categories, this scorecard becomes a tool to predict and track the advancement of humanoid robots. For example, over time, autonomous decision-making and empathetic algorithms may advance at rates parallel to sensor and processing capability, much like the hardware improvements charted in Moore’s Law for computing.

Underlying Industries (Core to Humanoid Robots)

1.Artificial Intelligence (AI) and Machine Learning:

  • Moore’s Law Analogy: AI models have been doubling in complexity and capability approximately every 18 months, driven by advances in computational power and training data availability.

  • Trajectory: Exponential growth in autonomous decision-making, empathy algorithms, and creativity will continue as more data and computing power are integrated into robot systems.

2. Sensing Technologies:

  • Moore’s Law Analogy: Sensor precision and diversity (e.g., vision, hearing, touch) have improved dramatically every two years, becoming more sensitive and affordable.

  • Trajectory: Sensors will move from "human-like" to "superhuman" in scope, with advancements in non-visible spectrums, brainwave detection, and more.

3. Actuation and Robotics Engineering:

  • Moore’s Law Analogy: Actuator power, range of motion, and precision are improving at a rate similar to advances in battery technology, which doubles in energy density roughly every 5 years.

  • Trajectory: As actuators become more efficient, robots will achieve higher speeds, strength, and flexibility.

4. Control Systems and Operating Systems:

  • Moore’s Law Analogy: Processing speed and control systems have followed a doubling of capability every 18-24 months, mirroring general advances in computing.

  • Trajectory: Control systems will integrate more fluid decision-making and interaction capabilities, pushing towards true autonomous behavior.

5. Energy and Battery Technologies:

  • Moore’s Law Analogy: Battery energy density doubles every 5 years, extending robot mobility and usage time.

  • Trajectory: Energy efficiency and storage will improve, extending operational times for humanoid robots.

Adjacencies and Future Impact:

  • Societal Integration: As robots advance, their societal roles will evolve from assistants to co-creators. This mirrors the “Freedom as a Function of Risk” theory, where robots are granted more freedom based on their learning capacity.

  • Democratization: Humanoid robots must be democratized, ensuring access for all. This could mirror shifts in the personal computer or smartphone revolutions, where cost and accessibility dropped sharply after an initial period of market domination.

We can identify several key underlying and adjacent industries that influence the evolution of humanoid robots. For each, we can hypothesize a “Moore’s Law”-like pattern of growth and project its trajectory over the next 10 years. Here’s a breakdown:

Adjacent Industries (Critical to Humanoid Robot Ecosystems)

1. Cloud Computing and Edge Computing:

  • Moore’s Law Analogy: Cloud and edge processing capabilities double in speed and efficiency every two years, allowing more real-time processing and lower latency.

  • Trajectory: Edge computing will allow robots to process more complex tasks locally while staying connected to cloud data for long-term learning and storage.

2. 3D Printing and Advanced Manufacturing:

  • Moore’s Law Analogy: 3D printing has been growing exponentially in terms of resolution, speed, and material strength, doubling in capability every two to three years.

  • Trajectory: Customization and production of humanoid robots will become cheaper and more flexible as advanced manufacturing evolves, enabling robots tailored to individual needs.

3. Ethics and Legal Frameworks:

  • Moore’s Law Analogy: Regulatory adaptation tends to lag behind technology but accelerates as industries mature. The development of ethics and legal frameworks doubles in complexity every 4-5 years.

  • Trajectory: Ethical guidelines and legal infrastructures will evolve to manage autonomous decision-making, privacy concerns, and societal impacts.

4. Healthcare and Assistive Technology:

  • Moore’s Law Analogy: Medical robots and assistive technologies are growing at a compound annual growth rate (CAGR) of 15-20%, driven by aging populations and healthcare innovation.

  • Trajectory: The integration of humanoid robots in healthcare will increase, especially for assistive care, diagnostics, and surgery.

Tracking and Plotting the Trajectories

1. Y-axis: Represents “capability” or “efficiency” depending on the industry, indexed against today’s capability at 1.

2. X-axis: Represents time in years, from 2024 to 2034.

Each line on the graph would represent one industry’s trajectory:

  • AI and Machine Learning: Exponential rise.

  • Sensing Technology: Steady but strong growth towards superhuman capabilities.

  • Actuation and Robotics Engineering: Rapid improvements but limited by mechanical and energy challenges.

  • Control Systems/OS: Strong exponential growth as more autonomy and creativity are integrated.

  • Battery Technology: Steady but slower growth, limiting mobility.

  • Cloud/Edge Computing: Sharp increase due to constant improvements in processing and data handling.

  • 3D Printing: Significant growth, but with more potential in customization rather than performance.

  • Ethics/Legal: Slower but consistent growth as frameworks catch up.

  • Healthcare Integration: Steady adoption of humanoid robots.

Observations:

  • AI, Machine Learning and Cloud Computing show steep, exponential growth, representing how quickly advancements are likely to occur in those areas.

  • Battery Technology and Ethics/Legal evolve more slowly, which could indicate potential bottlenecks in robot mobility and regulation.

  • The aggregate line (in black) combines all trajectories, showing a strong upward trend over the next 10 years.