Humanoid Robot Survival in Public Spaces: From Electrical Topology to Fault Injection

In recent years, there has been a lot of discussion about humanoid robots, but most of the topics have focused on high-profile parameters such as degrees of freedom, dexterous hands, and large model reasoning. After actually deploying a humanoid robot in public spaces such as malls, subway stations, hospital lobbies, and office front desks for three months, you will realize a harsh fact: the key factor determining whether a humanoid robot can be scaled for real-world deployment is often not how smart it is, but whether it can survive in such a random and disruptive environment. Recently, the industry has been discussing the direction of building a standard system for humanoid robots, and we also take this opportunity to summarize several opinions. The core theme is one: the bottom-line issues for a robot's survival in public spaces. This article will openly discuss the engineering logic, real-world test data, and lessons learned behind these opinions, hoping to provide some reference for teams working on similar projects or planning to deploy in public spaces.

1. Why the ability to survive in public spaces is more urgent than peak performance

1.1 The gap between the lab and public spaces is much bigger than imagined

Our test prototype ran for nearly four months in the lab before entering real-world environments. The lab floor was flat anti-static flooring, with constant lighting, no obstructions, and a clean wireless environment, like a blank sheet of paper. At that time, we were confident in the overall stability of the robot, thinking that "as long as the algorithm is good, problems outside won't be too bad." However, on the first day of trial operation in a commercial complex, a series of issues that could not be reproduced in the lab were exposed: the height difference between tiles caused frequent abnormal foot landing postures, sunlight passing through the glass curtain wall directly caused some visual detection to temporarily fail, the mall's announcements and the noise from the crowd made voice interaction almost unusable, and a janitor pushing a cleaning cart almost tripped the robot.

At that moment, I realized one thing: our evaluation system for robots is basically built on the assumption that "the environment cooperates with the robot," while the real logic in public spaces is "the robot cooperates with the environment." This means that the focus of standard discussions should not only be on motion performance and intelligence level, but also include a whole new dimension dedicated to environmental adaptability and fault survival capabilities.

1.2 The unique "non-cooperative interference sources" in public spaces

The biggest difference between public spaces and the lab is the presence of a large number of unpredictable, unmodelable non-cooperative interference sources. In lab testing, we would place obstacles and set pedestrian density according to specifications, but in real scenarios, the interference sources are random: a child might suddenly sit in front of the robot, a pet dog might bark at the robot, a shopping cart might be inserted into the robot's path, or someone might even reach out to touch the robot out of curiosity.

Individually, these interferences are not fatal, but when combined, they continuously consume the system's tolerance budget. For example, a visual algorithm processing a small toy on the ground might take an extra 100 milliseconds; an IMU experiencing a sudden collision might cause the posture data to change, leading to a re-convergence in gait planning; a microphone array being briefly affected by a child's scream might require the interaction process to restart. Our statistics show that in continuous operation in public spaces for 8 hours, a robot encounters an average of 2.3 "non-cooperative interferences" per hour, with about 17% triggering an emergency stop or protective shutdown. This ratio, if not forcibly reduced by standard setting, would be unacceptable for any operator.

1.3 The 'Survivability Metrics' Often Overlooked in Standard Discussions

Performance metrics are often well-defined, such as peak walking speed, maximum load, and continuous operation time. However, 'survivability metrics' have long lacked consensus. Here, the term 'survivability metrics' refers to the ability of the entire system to resist anomalies and recover from them. It at least includes the following items:

• MTBF (Mean Time Between Failures): The actual fault interval in public space environments, rather than the numerical value from laboratory settings.
• Self-recovery Time: The time from triggering protective shutdown to re-entering a serviceable state.
• False Trigger Rate: Includes false triggering of safety emergency stop, false recognition of obstacles, and false response to voice commands, etc.
• Power Failure/Fault Safety: Whether the system can maintain structural stability and not collapse or hit people in case of unexpected power failure.
• Environmental Tolerance Boundary: The maximum tolerance for sudden changes in lighting, slippery ground, and temperature fluctuations.

We recommend adding a separate dimension called "Public Space Survivability" in the standard system, and these metrics must be defined under real-world conditions rather than ideal conditions. Otherwise, we will face an embarrassing situation where laboratory tests show all green, but the robot crashes on the first day of deployment.

2. Opinion 1: The electrical topology structure must be included in the reliability requirements

2.1 Public Space Power Outage Matrix: The number one underestimated threat

After running in public spaces for a while, we will find that the most fatal threat to humanoid robots is not algorithm failure, but electrical issues. The power supply environment in public spaces is far less clean than in the laboratory. We have encountered several typical electrical faults:

• Battery hot swapping causing bus voltage drop , leading to the entire system rebooting instantly.
• Back electromotive force interference from joint motor stall , which directly disrupts CAN bus communication.
• Ground potential difference between different devices , causing the control board to reset during charging or when externally debugging.
• Improper power sequencing , where the current surge during the startup of multiple DC-DC converters triggers overvoltage protection.

The common point of these issues is: the robot doesn't necessarily have to be performing a high-level action when it fails; instead, it often suddenly "dies" while in standby, low-speed walking, or during charging. Simply relying on "using better power modules" is not enough. The rules must be set during the overall electrical architecture design phase.

2.2 What Exactly Is the Electrical Topology System Solving?

The term "humanoid robot electrical topology system" has been frequently mentioned in industry discussions recently. Essentially, it treats the humanoid robot as a distributed electrical system rather than just "battery plus a bunch of motors" in a simple series. A standard humanoid robot's electrical topology includes at least five levels:

We have disassembled several mainstream open-source humanoid robot prototypes, and found that the most common lazy design is: the perception layer directly draws power from the power bus, and the communication cables and power cables are run in the same cable tray. This approach is fine for short-term demo demonstrations, but in public spaces, it frequently causes sensor data glitches and communication packet loss during continuous operation, making troubleshooting extremely painful.

2.3 We recommend the following topology design: dual bus plus independent protection for critical loads

Based on our actual deployment experience, we recommend that the electrical topology of humanoid robots adopt a 'dual bus' architecture: one power bus dedicated to supplying power to the joint motors and drivers, and one control bus that independently powers the main control, perception, and communication modules. The two buses are connected through an isolated DC-DC converter, and the control bus side is equipped with a super capacitor or a small-capacity backup battery to ensure that the control side has at least 3 to 5 seconds of time to complete safe posture maintenance and state saving when the power side suddenly loses power.

At the same time, all critical loads must have independent overcurrent, overvoltage, and reverse polarity protection, and cannot share a single fuse or a single electronic switch. In public spaces, users may use third-party chargers, debugging equipment, or even mistakenly plug in other power sources. If interface protection is not done properly, a single accident can burn out the entire main control board.

2.4 Real-world case: troubleshooting a CAN bus being knocked offline by motor back-EMF

This issue is worth recording separately. Our prototype machine experienced intermittent left leg joint loss of control during testing in the second week, occurring roughly every half hour, with the phenomenon being occasional joint shaking followed by a communication timeout. Initially, we suspected poor CAN line contact, but replacing with a shielded cable did not improve the situation; later, we suspected the main control's CAN transceiver was faulty, but replacing it still resulted in random occurrences.

Finally, we used an oscilloscope to simultaneously capture the bus voltage of the motor driver and the CAN differential signal, and we located the problem: when the left leg hip joint was under high torque output, the reverse voltage spike on the driver bus reached over 80V. Although the duration was extremely short, it induced interference pulses exceeding the common-mode range of the transceiver through cable coupling on the CAN bus, leading to communication errors. The solution is not complicated: adding a TVS diode and absorption capacitor on the driver bus side, and changing the CAN cable to a twisted-pair cable with an independent shielding layer and single-ended grounding, which eliminated the failure rate completely.

This issue is difficult to expose in traditional performance testing because it only occurs under specific loads and specific cable layouts. If the standards do not include explicit requirements for electrical topology structure, cable isolation, and transient interference suppression, robots deployed in public spaces will be in a state of 'sometimes working, sometimes not' long-term, and this state consumes the most maintenance effort.

3. The second and third opinions: the perception system should define the 'minimum usable threshold' in public spaces

3.1 Why public spaces are hell mode for perception

If electrical systems are the foundation of survival, perception is the gate to interaction in public spaces. However, the perception environment in public spaces is almost the opposite of the lab. The lighting in the lab is uniform and stable, while in public spaces, there are large areas of glass curtain wall reflections, flickering light boxes, direct sunlight from outdoors, and mixed light sources indoors. Our visual lead once said: 'In the lab, we tune for the upper limit, but in public spaces, we first have to defend the lower limit.' This lower limit is the system's ability to safely complete basic tasks under these harsh conditions.

Sudden changes in lighting are the biggest threat to vision. When a robot moves from a mall entrance into a atrium, the brightness can change by several EVs in 0.5 seconds, and the automatic exposure cannot catch up, leading to temporary overexposure or underexposure of the image. If the robot is walking at this time, depth estimation may become unstable for hundreds of milliseconds, leading to path jittering at best, or collision with low obstacles at worst. We have tested this with standard test cards, and in backlit and strong reflective scenarios, the target detection confidence of ordinary RGB cameras drops by about 30% on average.

3.2 The SNR challenge for microphone arrays in public spaces

"Humanoid robot microphone array" has become a popular keyword, indicating that the industry is starting to take voice interaction seriously. However, the performance of a microphone array in public spaces is far more complex than the parameters listed in a spec sheet. Specifications are usually measured in anechoic chambers, while real-world environments in malls typically have ambient noise levels between 65 to 75 decibels, and can exceed 80 decibels during peak hours, plus announcements, music, and conversations, making the signal-to-noise ratio difficult to manage.

We identified several specific pain points in our testing:

• Proximity saturation: When users speak close to the microphone, signal clipping causes a drop in recognition rate.
• Acoustic path change: When there are glass partitions or metal decorations near the robot, multi-path reflections can make echo cancellation algorithms fail.
• High false wake rate: Broadcasts or promotional phrases in mall announcements can easily trigger the wake word.

To address these issues, we proposed two layers of suggestions for the standard. The first layer is to define the "minimum usable conditions" for voice interaction, such as a wake-up success rate of no less than 95% in a 75 dB noise environment, at a distance of 3 meters, and with normal speaking volume. The second layer is to require microphone arrays to have dynamic range management and anti-saturation capabilities, so they don't fail entirely when encountering loud sound pressure levels.

3.3 We suggest specific perception threshold parameters

Taking into account these real-world testing experiences, we have compiled a set of recommended threshold parameters for the perception system of humanoid robots deployed in public spaces, for reference by peers:

3.4 Test Matrix for Visual-Microphone-IMU Multimodal Fusion in Public Spaces

A single perception modality is insufficient in public spaces. What truly enables the system to operate stably is multimodal fusion. However, fusion is not simply combining output results, but establishing cross-validation and priority arbitration between different modalities. For example, if vision detects an obstacle ahead but LiDAR shows it is glass (point cloud penetration), the more conservative result should be taken. If voice recognition detects the command "go forward" but vision detects a child within 1 meter, the system must refuse to execute.

We built a test matrix for public spaces, listing each single-modal failure scenario and testing whether the fusion algorithm can cover them:

• Scenario A: Visual detection is lost due to strong light interference, with microphone array and IMU providing backup references.
• Scenario B: High environmental noise makes voice unusable, and the system takes over interaction through visual intent recognition (gestures, orientation).
• Scenario C: Wet ground causes IMU data anomalies, and visual and foot force sensors jointly correct posture estimation.

After running this test matrix, our biggest takeaway is: don't trust any single modality's "high indicators." The goal of multimodal fusion is not to make every indicator look good, but to ensure the system can make decisions that don't harm people or damage itself even in the worst-case scenarios.

4. The Fourth Opinion: Open-source hardware interfaces should be the interoperability baseline

4.1 The value of an open-source humanoid robot lies in reducing the cost of trial and error

Deploying in public spaces presents a very practical issue: no team can tackle all aspects in isolation. Mechanical structure, joint actuation, motion control, perception fusion, and interaction logic all require extensive validation and iteration. This is where the value of an open-source platform becomes evident. Like the recently discussed "open-source humanoid robot Hunter," it doesn't provide a perfect robot, but rather a complete, modifiable baseline system: open-source structural drawings, open joint communication protocols, a customizable motion control framework, and accompanying simulation environments. Teams can run a basic demo within days and focus their efforts on solving the specific problems they need to address.

Our prototype machine was also based on an open-source platform. The underlying bipedal kinematics and joint actuation modules were directly adopted from the open-source solution, and we mainly modified three aspects: re-wiring the electrical topology, replacing the sensor fusion framework, and customizing the interaction logic. Without this baseline, just getting the mechanical and actuation systems to work would take up most of the time, leaving no room for specialized testing of the robot's ability to survive in public spaces.

4.2 Why standards should include open-source interfaces

If a standard system only covers "whole-machine performance" without involving "component interfaces," it can lead to a problem: the supply chain is bound to a few whole-machine manufacturers, making it difficult for third-party sensors, computing modules, and actuators to connect, resulting in high innovation costs. We recommend defining key interface layers as "recommended interoperability baselines" in the standard, including:

• Joint Module Interface: Standardize mechanical flange size, communication protocols, and power supply specifications.
• Computing Unit Interface: Define physical and logical interface standards between the main control board and expansion boards.
• Sensor Interface: Establish common data formats and time synchronization mechanisms.
• Simulation Interface: Specify the method for converting real machines to simulation models.

With this, different teams' sensor modules, control algorithms, and even whole-machine subsystems can be quickly replaced and validated under a unified interface. Public spaces are diverse, and no single solution can adapt to all scenarios. The more open the interface, the easier the collaboration between upstream and downstream teams, and the more controllable the cost in actual projects.

4.3 Practical details for extending on an open-source platform

Secondary development on an open-source platform doesn't mean using it directly; there are many details to handle in between:

• Power Tree Reconstruction: The original power board only ensured basic power supply, so we redesigned a power distribution board, isolating the perception system separately and adding overcurrent protection for USB interfaces.
• Motor Drive Shielding: The original sample machine's wiring was basically "exposed," so we added a braided shielding sleeve around the motor drive wiring and applied three-proof glue at the connector locations to prevent short circuits caused by common condensation and cleaning water in public spaces.
• Sensor Mounting Structure: The original microphone array was directly attached to the internal shell, but testing showed that shell resonance significantly affected voice capture. We specially designed a vibration-damping bracket to isolate the array from the structural components, improving the voice recognition rate by about 15%.

These modifications are rarely found in the Issue area or technical documentation of the open-source community, and basically belong to "experience gained after running." If the standard could consolidate these types of "public space adaptation modifications" into recommended guidelines, it would provide direct help to subsequent teams.

4.4 Maintenance costs caused by non-unified interfaces may exceed your expectations

We have a statistic: due to the lack of unified sensor interface protocols, replacing or debugging sensors requires rewriting drivers and calibration programs, with an average time cost of about 20% of the total project development time. Once sensors are upgraded or replaced with a different brand, this time is repeated. Public space deployment has different requirements for sensors compared to the lab, and may require frequent replacement of different models to adapt to seasonal, lighting, and foot traffic changes. Interface standardization essentially saves all project teams time and budget, and this value should be included in the goals of standard system construction.

5. The Fifth and Sixth Opinions: Fault survival and remote maintenance are the long-term race in public spaces

5.1 Fault escape protocol: falling, power failure, and collision cannot be chaotic

Public spaces are different from factories. In factories, robot failures can be handled by on-site engineers, but in malls or hospitals, robots face a group of people with no technical background. Therefore, fault behavior and escape strategies must be clearly defined in advance, and the standard should be "understandable to ordinary people and not causing injury."

The fault escape protocol we defined includes three layers:

• Fall self-recovery strategy: After detecting an abnormal posture, first determine the direction of the fall. If the surroundings are safe, attempt to stand up autonomously; if stuck and unable to stand up, do not blindly try repeatedly, but immediately reduce joint output to avoid motor stalling overheating and gear damage.
• Power failure safety strategy: When the main power is abnormal, all joints with brake locks immediately lock, and joints without brakes slowly release using gravitational potential energy to ensure the robot lands in a "controlled soft tissue landing" posture, rather than crashing onto the ground.
• Collision flexibility strategy: After collision detection is triggered, do not directly enter hard emergency stop, but first enter the "flexible resistance" mode, allowing the joints to actively retreat a certain distance to buffer the impact, and then decide whether to completely stop based on the force.

The biggest value of this protocol is to protect both the robot and people. Although hard emergency stop responds quickly, in crowded environments, it may cause the robot to lose balance and crash into nearby people, making flexible handling safer.

5.2 Public space testing must include a "fault injection" phase

Conventional functional testing can only prove that "no errors occur under normal conditions," but it cannot demonstrate "whether the system can withstand abnormal situations." We added a fault injection testing process to our testing plan, actively creating problems to verify the system's robustness:

• Communication interruption injection: Randomly disconnect the CAN communication of a single joint, observing whether the system can detect and enter a safe state within the specified time.
• Sensor failure injection: Blocking the camera lens, adding noise to the microphone array, or freezing the IMU output, testing whether the fusion algorithm can quickly switch to a backup strategy.
• Power drop injection: Randomly lowering the bus voltage to the under-voltage threshold for a period of time, verifying whether the backup power supply can take over the control bus.
• External interference injection: Using soft objects to push the robot from different directions, testing whether it can maintain balance or actively lower its center of gravity under non-lethal external forces.

This type of testing is highly worthwhile to run thoroughly before deployment in public spaces. One typical issue we found during fault injection testing was: the system had a significant delay in determining that the 'sensor completely failed', triggering protection only when visual loss exceeded 2 seconds. The reason was that the algorithm layer over-smoothed abnormal data, causing abnormal features to be 'washed out'. Later, we adjusted the health monitoring logic, placing anomaly detection before fusion, reducing the response time to within 300 milliseconds.

5.3 Remote Maintenance Loop: The 'Last Mile' of Public Space Operations

Public space operations cannot guarantee the presence of engineers on-site, so remote maintenance capabilities must be designed from the beginning. In our project, we built a closed-loop process:

• Edge Logs: The robot continuously records system status, sensor raw data, and algorithm key outputs locally, with circular storage of the most recent 30 minutes of data.
• Abnormal Data Transmission: When a fault occurs, logs, screenshots, and posture records are automatically packaged and transmitted back to the remote platform, along with a time-series data segment of 5 seconds before and after the fault.
• Remote Diagnosis: Maintenance personnel can view the status, issue diagnostic commands, and read real-time joint temperature, current, and other fields through the remote platform.
• OTA Upgrade: For software defects and algorithm parameters, upgrades are pushed in batches through remote channels, and the robot automatically installs and self-checks during idle times.

This closed-loop process seems not complex, but there are many pitfalls in actual deployment. Public space WiFi network quality varies greatly, and commercial public WiFi often has Portal authentication and bandwidth limits. The robot's own hotspot signal decays quickly under crowd blockage. Our final approach was to retain 4G/5G modules as backup channels, compress data formats as minimally as possible, and keep each fault transmission within 5MB, prioritizing the complete delivery of core logs.

5.4 Summary of Six Suggestions

We compiled the key points of the previous suggestions into a summary table for easy reference:

6. Test Review: Pits and Data from Three Months of Public Space Testing

6.1 Test Site and Overall Data

We conducted cumulative three months of testing in a medium-sized commercial complex, covering workdays and weekends in the morning, afternoon, and evening periods, with pedestrian density ranging from less than 0.5 people per square meter in the morning to over 2 people per square meter in the evening. The testing content included common service scenarios such as autonomous driving cruise, fixed-point explanation, voice consultation, and guidance.

The cumulative data over three months showed: the prototype machine ran an average of about 9 hours per day, with an average daily travel distance of about 6 kilometers, and the total number of triggered safety shutdowns decreased from 6 to 7 times per day in the first week to 1 to 2 times per day in the 12th week. This improvement mainly came from three directions: electrical topology reconstruction, perception algorithm parameter adjustment, and fault recovery logic optimization. However, this also indicates a problem: pure software optimization can solve some issues, but the shortcomings at the electrical architecture and mechanical structure levels are difficult to fully compensate for through later algorithm adjustments.

6.2 Top 5 Typical Faults

The following are the five most frequently encountered types of failures we encountered during public space testing, along with targeted solutions:

6.3 The most memorable full-day continuous operation test

Once we conducted a full-day test without intervention, only allowing remote monitoring, without on-site personnel handling. In the morning, everything went smoothly, and the robot completed 10 rounds of patrol in the atrium, with normal voice interaction. By noon, sunlight began to directly hit the entrance corridor area, and the robot experienced two visual anomalies while passing through an 8-meter-long light transition zone, causing short-term path planning stagnation. The robot later switched to laser radar and IMU fused data to avoid any issues. In the afternoon during peak pedestrian traffic, about 11 pedestrians actively touched the robot, with 3 instances triggering emergency stop, and once the trigger position was on the back, the robot could not recover autonomously for a short time, so we had to remotely issue a command to have the joints enter a low-power state, waiting for staff assistance.

This test left us with the greatest impact: robots in public spaces can never rely solely on themselves to complete everything. Their form, interaction methods, and failure manifestations must be designed to be "understandable and cooperative" by ordinary people, rather than requiring ordinary people to adapt to the robot's logic.

6.4 Operation checklist for teams preparing to conduct similar tests

If your team is also planning to deploy a humanoid robot in a public space for testing, there are several operational recommendations you can prepare in advance:

• First conduct an electromagnetic environment and WiFi survey of the site, bring a spectrum analyzer and network testing tools to walk around, and record signal blind spots.
• Prepare a "shadow recorder", follow the robot to simultaneously capture first-person video and sensor data, for use in fault analysis.
• Treat all exposed interfaces and sensors with waterproof and dustproof measures, as the cleaning process in public spaces is far more frequent than imagined.
• Establish a standard fault grading system, distinguish between "recoverable remotely", "requiring on-site intervention", and "must be repaired in the factory" levels, to avoid firefighting maintenance.
• Conduct a weekly fault trend analysis, don't just look at single events, but observe the change curve of fault frequency.

After three months of testing, my personal deepest understanding is: the survival of humanoid robots in public spaces is not tested by the extreme breakthrough of a single technical aspect, but by the system's ability to maintain basic decency under the worst conditions. This "decency" behind it is the redundancy design of the electrical architecture, the bottom-line thinking of the perception system, an open collaboration ecosystem, and a practical and reliable fault survival mechanism. If the standard system can fully incorporate these aspects, giving every robot entering public spaces a common "survival底线", then this direction is truly on the right track.