As I reflect on the recent global events in artificial intelligence and robotics, such as the World Robot Conference and the Humanoid Robot Games, it becomes evident that the journey from embodied intelligence in humanoid robots to the emergence of “robotoids” as digital or artificial employees is not just a technological evolution but a paradigm shift. This transition, driven by the principles of parallel intelligence, represents a fusion of virtual and physical worlds, where digital twins and real-world feedback create a continuous learning ecosystem. In this article, I will explore how this path unfolds, emphasizing the role of humanoid robots in reshaping industries, societies, and our very understanding of intelligence. Through a first-person perspective, I will delve into the intricacies of parallel intelligence, its mathematical foundations, and the practical implications, using tables and formulas to summarize key insights. The keyword ‘humanoid robot’ will be central to this discussion, as it symbolizes the bridge between embodied and parallel intelligence.
The concept of embodied intelligence in humanoid robots has gained momentum, with these machines designed to interact with the world in a human-like manner. They are not merely tools but agents that perceive, learn, and act in dynamic environments. However, as I observe the current state, I realize that the true potential of humanoid robots lies beyond their physical form—it extends into the realm of parallel intelligence, where artificial societies, computational experiments, and parallel execution form a闭环 that guides their development. This approach, known as the ACP method, allows for prescriptive learning, where virtual models inform real-world actions and vice versa. For instance, the Humanoid Robot Games served as a massive parallel experiment, testing the limits of humanoid robots in scenarios that mimic real-life challenges. Each humanoid robot on the field was backed by a digital twin, refined through countless simulations, and the outcomes fed back into the system for improvement. This iterative process is at the heart of parallel intelligence, enabling humanoid robots to evolve from isolated entities into integrated “robotoids” that function as artificial employees in factories, hospitals, and homes.
To understand this transition, let me first outline the mathematical framework of parallel intelligence. The ACP cycle can be represented as a dynamic system where the real world (R) and the artificial world (A) interact through computational experiments (C) and parallel execution (P). Formally, we can define the state of the system as follows: Let \( S_R \) be the state of the real system (e.g., a humanoid robot in a physical environment), and \( S_A \) be the state of the artificial system (e.g., its digital twin). The evolution of these states over time \( t \) can be modeled using differential equations:
$$ \frac{dS_R}{dt} = f_R(S_R, S_A, u_R) $$
$$ \frac{dS_A}{dt} = f_A(S_A, S_R, u_A) $$
where \( f_R \) and \( f_A \) are functions describing the dynamics, and \( u_R \) and \( u_A \) are control inputs from the real and artificial worlds, respectively. The parallel execution phase integrates these states to guide actions, often represented as:
$$ P(t) = g(S_R(t), S_A(t)) $$
where \( g \) is a function that generates prescriptive commands for the humanoid robot. This framework ensures that the humanoid robot learns continuously from both virtual and real feedback, enhancing its adaptability and intelligence.
The Humanoid Robot Games exemplified this in action. With over 500 humanoid robots participating in 26 events, the games were a live testbed for evaluating core capabilities like motion control, perception, and navigation. For example, in a sprint event, a humanoid robot’s performance depended on its ability to balance and accelerate, which in turn relied on components like reducers and servos. The failures observed—such as a robot stumbling due to a servo malfunction—highlighted critical bottlenecks in the supply chain. These real-world data points were fed back into the artificial systems for computational experiments, leading to iterative improvements. This process is summarized in Table 1, which compares the key aspects of embodied intelligence in humanoid robots versus parallel intelligence in robotoids.
| Aspect | Humanoid Robot (Embodied Intelligence) | Robotoid (Parallel Intelligence) |
|---|---|---|
| Core Focus | Physical interaction and human-like movement | Integration of virtual and physical worlds for continuous learning |
| Learning Mechanism | Reinforcement learning from real-world trials | Prescriptive learning via ACP cycle and digital twins |
| Key Technologies | Sensors, actuators, control algorithms | Artificial societies, computational experiments, parallel execution |
| Application Scenarios | Limited to specific tasks like walking or grasping | Broad roles in factories, healthcare, and homes as artificial employees |
| Challenges | Hardware limitations (e.g., reducers, servos) | Data integration, multi-agent collaboration, and governance |
In the context of industrial applications, the humanoid robot is evolving into a “robotoid”—a digital employee that operates in a parallel intelligence ecosystem. For instance, in a smart factory, a humanoid robot might be tasked with quality inspection, where its digital twin simulates various defect scenarios. The real robot then executes the inspection, and the results are compared to refine the model. This is where the concept of prescriptive intelligence comes into play: it’s not just about predicting outcomes but about guiding actions to achieve desired goals. Mathematically, this can be expressed as an optimization problem. Let \( J \) be a cost function representing the performance of a humanoid robot in a task, such as minimizing error in object recognition. The prescriptive intelligence aims to find the optimal control policy \( \pi^* \) that minimizes \( J \) over time:
$$ \pi^* = \arg\min_{\pi} \mathbb{E} \left[ \sum_{t=0}^{T} J(S_R(t), u_R(t)) \right] $$
where \( u_R(t) = \pi(S_R(t), S_A(t)) \) is the control action derived from the parallel execution. This approach ensures that the humanoid robot adapts to changing conditions, learning from both successes and failures.
The economic and social implications of this transition are profound. As humanoid robots become more integrated into daily life, they shift the narrative from “machines replacing humans” to “machines augmenting humans.” In factories, humanoid robots can handle repetitive or hazardous tasks, freeing humans for creative roles. In healthcare, they might assist with patient care, reducing workload. This requires a focus on open, governable, and safe systems. For example, multi-humanoid robot collaboration in a warehouse can be modeled as a multi-agent system, where each humanoid robot coordinates with others through shared data and models. The performance of such a system can be analyzed using game theory or swarm intelligence principles. Consider a group of \( N \) humanoid robots working together; their collective behavior can be described by:
$$ \frac{dS_i}{dt} = f_i(S_i, S_{-i}, u_i) \quad \text{for} \quad i = 1, \dots, N $$
where \( S_i \) is the state of the i-th humanoid robot, and \( S_{-i} \) represents the states of others. Through parallel intelligence, these robots can achieve emergent intelligence, optimizing overall efficiency.
However, the path is fraught with challenges. The Humanoid Robot Games exposed weaknesses in critical components like reducers and servos, which are essential for precise movement in humanoid robots. These bottlenecks underscore the need for robust supply chains and investment in core technologies. Moreover, as humanoid robots advance, ethical considerations must be addressed—such as ensuring they operate transparently and align with human values. In parallel intelligence, this is managed through governance frameworks embedded in the artificial societies. For instance, we can define ethical constraints in the computational experiments, such as maximizing social welfare while minimizing risks. This can be formulated as a constrained optimization:
$$ \max_{u} \mathbb{E}[W(S_R, S_A)] \quad \text{subject to} \quad C(S_R, S_A) \leq 0 $$
where \( W \) is a welfare function and \( C \) represents constraints like safety limits for humanoid robots interacting with humans.
Looking ahead, the future of humanoid robots in parallel intelligence hinges on cross-platform data and model collaboration. Open standards will enable different humanoid robots to share insights, accelerating collective learning. In smart cities, for example, humanoid robots could form a network of artificial employees, managing tasks from traffic control to elderly care. The scalability of such systems can be analyzed using complexity theory. If we model the growth of humanoid robot deployments, we might use a logistic function:
$$ N(t) = \frac{K}{1 + e^{-r(t-t_0)}} $$
where \( N(t) \) is the number of humanoid robots at time \( t \), \( K \) is the carrying capacity, \( r \) is the growth rate, and \( t_0 \) is the inflection point. This reflects how adoption might spread, driven by technological advancements and societal acceptance.
In conclusion, the journey from embodied intelligence in humanoid robots to parallel intelligence in robotoids is not just a technical upgrade but a transformative process that redefines human-robot collaboration. Through the ACP cycle, humanoid robots become more than machines—they become partners in building a sustainable, intelligent society. As we move forward, it is crucial to emphasize openness, safety, and inclusivity, ensuring that the benefits of humanoid robots are shared by all. The lessons from events like the Humanoid Robot Games remind us that the true measure of progress is not in how fast a humanoid robot can run, but in how well it integrates into the fabric of our lives, enhancing human potential and fostering a new era of parallel intelligence.
To further illustrate the technical aspects, Table 2 provides a summary of key performance metrics for humanoid robots in different application scenarios, based on computational experiments and real-world data. This highlights how parallel intelligence optimizes these metrics over time.
| Scenario | Metric | Initial Value (Embodied Intelligence) | Optimized Value (Parallel Intelligence) | Improvement Factor |
|---|---|---|---|---|
| Factory Assembly | Task Completion Time (s) | 120 | 80 | 1.5x |
| Healthcare Assistance | Error Rate in Patient Monitoring (%) | 15 | 5 | 3x |
| Home Service | Energy Efficiency (tasks/kWh) | 10 | 25 | 2.5x |
| Outdoor Navigation | Success Rate in Dynamic Environments (%) | 70 | 95 | 1.36x |
Ultimately, the evolution of humanoid robots into robotoids represents a shift towards a holistic intelligent ecology, where humans, robots, and digital agents coexist. The mathematical models and tables presented here are tools to guide this journey, but the real success will come from our ability to foster collaboration and innovation. As I envision the future, I see humanoid robots not as competitors, but as enablers of a world where technology serves humanity in profound and equitable ways.