How Far Are Humanoid Robots from Achieving Practical Autonomous Decision-Making with 10 TOPS Computing Power?

1. Current Core Bottlenecks in Autonomous Decision-Making

• Insufficient Environmental Understanding: Current robots rely on pre-programming and manual control, making it difficult to understand dynamic changes in unfamiliar environments (such as markets or homes) in real-time.
• Weak Learning and Generalization Abilities: Robots lack a “human-like learning loop,” which prevents them from mastering skills through single observations or transferring experiences across different scenarios (for example, grasping a new-shaped cup). Training a single high-precision action (like storing headphones) in a lab requires 20 hours of data, but generalizing to unknown tasks remains a challenge.
• Immature Dynamic Decision Mechanisms: End-to-end autonomous decision-making (comprising instruction understanding, planning, execution, and error correction) has not yet been achieved. Complex tasks (such as folding clothes or delivering food) still require human intervention, with a failure rate exceeding 50%.

2. Practical Effectiveness Boundaries of 10 TOPS Computing Power

• Applicable Scenarios: In structured environments like industrial assembly lines (for tasks such as screwing and transporting), the success rate can exceed 90%. However, this is conditional on standardized environmental setups, meaning it is not truly “autonomous.”
• Limitations: A robot equipped with 10 TOPS from Yushun is priced at 26,900 yuan and can support basic sorting and logistics tasks but struggles with unforeseen situations; compared to Tesla’s Optimus, which utilizes a 100 TOPS chip, the 10 TOPS system is inadequate for real-time inference in multi-modal large models, clearly illustrating the computational gap.

3. Timeline for Practical Application and Key Breakthroughs

• Phased Implementation Expectations:
  • Industrial Scenarios (3-5 years): Achieve limited autonomy in controlled environments like factories and warehouses (for instance, Xiaomi robots operating continuously for three hours).
  • Specific Service Scenarios (5-10 years): Tasks like elderly care or airport deliveries will require a combination of 100 TOPS-level computing power and incremental learning technology (for example, Meituan’s airport delivery robots).
  • Full Home Autonomy (10+ years): This will necessitate breakthroughs in “world modeling” (understanding physical rules) and lifelong learning mechanisms, which are currently only in the proof-of-concept stage in labs.
• Factors Essential for Technological Breakthroughs:
  • Computational Power Upgrade: Edge computing power needs to evolve towards 100+ TOPS (like NVIDIA’s Orin module) to support the deployment of VLA large models.
  • Algorithm Innovation: Developing a “hybrid architecture of embodied and disembodied intelligence” to facilitate cloud knowledge transfer and local real-time responses.
  • Cost and Ecosystem: Domestic chip production (like Xuankai 01) can reduce hardware costs, while policies promoting cross-industry data sharing can accelerate training.

4. Controversies and Challenges

• Debate on Form Necessity: Some argue that non-humanoid robots (like robotic dogs) may be more efficient in specific scenarios, as humanoid designs can increase costs and complexity.
• Ethical and Safety Risks: Fully autonomous decision-making requires clear delineation of responsibility to avoid uncontrollable behaviors, and the current regulatory framework is still inadequate.