PaliPali

In my previous discussion on humanoid robot development, I addressed the critical issue of RSOC safety. This time, I want to explore another major challenge: reliability, specifically system hang and abnormal shutdown.

From my experience in developing high-performance computing systems, such as gaming notebooks and enterprise-grade computing solutions, one of the most difficult aspects is working with next-generation chips. When both CPU and GPU are from new architectures, the likelihood of encountering BSOD, system hang, or unexpected shutdown increases significantly. Often, even after extensive debugging and validation, we still face intermittent issues that are nearly impossible to eliminate within a given development timeline. As a result, we set conditional shipment criteria, allowing developers to continue working on long-term fixes while still delivering products to market. While this approach is necessary, it does pose risks to users, even though we implement measures to prevent data loss and ensure recovery through system rebooting.

However, when it comes to humanoid robots, we must ask: how much failure PPM can the market accept? In consumer electronics, a system crash might be inconvenient, but in humanoid robots, a failure could lead to serious safety hazards. If a humanoid robot unexpectedly hangs or shuts down, it must not endanger the user’s well-being. This is a fundamental challenge that must be addressed before humanoid robots can be widely adopted in consumer markets.

Building trust in a product’s reliability requires rigorous stress testing. In the notebook and gaming PC industry, we subject products to extreme conditions to validate their robustness. Yet, when I observe current humanoid robot developments, I see an overwhelming focus on showcasing agility and movement precision. What I rarely see, however, is any discussion on reliability testing. How are these robots stress-tested for long-term durability? How are their hardware components, such as servo motors, validated for reliability? More importantly, how is software compatibility and fault tolerance ensured? In the near future, these concerns will become central to discussions on humanoid robot safety and usability.

Another pressing issue is the integration of control chips from different manufacturers within a humanoid robot. If these chips contain machine learning algorithms that are not well-synchronized, interoperability risks arise. AI models, especially those leveraging large language models (LLMs), often contain opaque mechanisms that developers struggle to fully control or understand. This opacity poses significant concerns for humanoid robot development. Without transparency in AI operations, predicting system failures becomes extremely difficult.

This reminds me of the Japanese manga Parasyte, where Shinichi defeats a group of parasites by introducing a toxic metal into their bodies. Since the parasites operate as independent entities without a central control mechanism, they collapse due to internal conflicts. Similarly, if multiple machine learning models exist within a single humanoid robot but lack a structured communication framework, unpredictable failures could occur. This is why establishing a clear AI architecture is crucial—not only for system stability but also for ensuring the overall safety of the device. Additionally, once a structured framework is in place, rigorous reliability testing becomes even more essential to validate the system’s ability to handle unexpected scenarios.

As we push the boundaries of AI-driven humanoid robots, we must recognize that reliability is not just a technical challenge—it is a fundamental requirement for consumer adoption. Ensuring a transparent AI architecture and implementing robust reliability testing will be key to mitigating risks and building a sustainable ecosystem for humanoid robotics.

隱藏的風險：人形機器人發展中的系統當機問題

在我先前關於人形機器人發展的討論中，我已談及 RSOC（相對電量狀態）安全性 這一重要議題。這次，我想進一步探討另一項重大挑戰：可靠性，特別是系統當機與異常關機的問題。

從高效能運算產品的經驗談起

以我過去在開發高效能運算系統（如電競筆電與企業級運算解決方案）的經驗來看，最困難的部分之一，就是與下一代晶片架構打交道。當 CPU 與 GPU 都來自新架構時，發生藍畫面（BSOD）、系統當機或異常關機的機率會大幅上升。即使經過長時間的除錯與驗證，我們仍然常常遇到無法在開發時程內完全消除的間歇性問題。因此，我們往往會設定條件式出貨準則，允許開發團隊在出貨後繼續追蹤與修復問題，同時確保產品能如期上市。這雖然是一種務實的做法，但對使用者而言，仍潛藏一定風險──即便我們已採取各種預防資料遺失的保護機制，並透過系統重啟確保恢復能力。

但人形機器人不能容許相同風險

然而，當我們面對的是人形機器人時，問題的嚴重性就必須被重新評估。我們必須問：市場可以接受多高的失效率（PPM）？

在消費性電子產品中，當機也許只是造成一時不便；但在人形機器人上，一次失效可能導致嚴重的安全風險。如果機器人在運作過程中突然當機或關機，必須絕對不能危及使用者的人身安全。這是人形機器人要進入消費市場前，必須率先克服的根本挑戰。

可靠性測試：信任的基石

要讓使用者信任機器人，必須建立在極為嚴格的壓力測試上。在筆電與電競 PC 領域，我們會將產品置於極端條件下進行驗證，以確保其穩定性與耐久性。然而，當我觀察目前的人形機器人發展趨勢時，看到的多是展示靈活動作與精密操控的影片，但幾乎沒有看到任何有關「可靠性測試」的討論。

• 這些機器人如何進行長時間耐久測試？
• 其核心零組件（如伺服馬達）是否經過可靠性驗證？
• 軟體的相容性與錯誤容忍能力如何確保？

我認為在不久的將來，這些問題將會成為人形機器人安全性與實用性討論的核心議題。

多晶片系統的 AI 協同挑戰

另一個值得重視的問題是：人形機器人內部往往整合來自不同廠商的控制晶片，若這些晶片中的機器學習演算法未能良好同步，將造成跨模組互通風險。目前許多 AI 模型，尤其是基於大型語言模型（LLMs）的系統，內部運作機制常常極為複雜甚至不透明，讓開發者難以完全掌握。這種「黑盒」式的結構，對於預測與防範系統失效，是一大風險。

這讓我想起日本漫畫《寄生獸》中的一幕：主角新一藉由將毒性金屬導入寄生獸體內，使得這些彼此無中央協調機制的寄生獸群體，因為內部衝突而自行瓦解。同樣地，若一台人形機器人內部擁有多個彼此缺乏溝通架構的 AI 模型，就極有可能發生難以預測的錯誤或衝突。

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建立透明 AI 架構與標準化介面是關鍵

因此，我主張：建立一個清晰的 AI 架構是關鍵，不只是為了系統穩定性，更是整體裝置安全性的保障。一旦有了這樣的架構，我們才能在上面實施系統性且嚴格的可靠性測試，確保即使面對突發情境，也能維持運作不中斷。

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結語：可靠性是消費市場接受的底線

隨著 AI 驅動的人形機器人持續邁進，我們必須清楚地意識到：可靠性不只是技術門檻，而是用戶接受與否的根本條件。唯有建立透明可控的 AI 架構，並落實全面的可靠性驗證，才能真正降低風險，打造出可永續發展的機器人產業生態系。