In homes with pets—especially dogs—one of the most dreaded robot vacuum failures isn’t a navigation error or a missed corner. It’s the dreaded “poop incident”: when your expensive smart cleaner rolls straight through an accident and smears it across half the house. This nightmare has become so common that online forums are filled with horror stories, photos, and dark humor about “poop mode” instead of “clean mode.” So, when comparing two of the biggest names in robotic vacuums—Roborock and Roomba—which brand actually avoids dog poop better?
The answer isn't just about suction power or battery life. It hinges on advanced sensors, intelligent obstacle recognition, and whether the robot can distinguish between a sock and something far less pleasant. As both brands have evolved their technology over recent years, this question has taken on new urgency—especially as pet ownership rises and consumers expect smarter, more autonomous devices.
How Robot Vacuums Detect Obstacles
To understand why some robots fail spectacularly at avoiding dog poop, we first need to examine how they perceive their environment. Most robot vacuums rely on a combination of sensors:
- Lidar (Light Detection and Ranging): Spins to create a map of the room using laser pulses. Excellent for navigation but limited in identifying object types.
- Camera-based vision systems: Some newer models use cameras to visually recognize objects. This is where AI comes into play.
- Bump sensors: Basic tactile feedback—when the robot hits something, it changes direction.
- Infrared and cliff sensors: Prevent falls down stairs but don’t help with ground-level obstacles.
Traditional robots only detect physical presence, not material composition. A soft pile of poop might not trigger bump sensors until contact occurs—and by then, it’s too late. The key differentiator now lies in visual recognition powered by artificial intelligence.
Roomba: iRobot’s Approach to Smart Cleaning
iRobot, the maker of Roomba, introduced its first AI-equipped model with the j7 series in 2021. Marketed specifically as “pet owner friendly,” the Roomba j7+ was the first robot vacuum to claim it could actively avoid pet waste. How? Through a front-facing camera and machine learning trained on thousands of images—including, yes, simulated dog poop.
According to iRobot engineers, the system uses a proprietary AI model called “PrecisionVision Navigation” that identifies high-risk objects in real time. When the robot detects something resembling poop, it steers around it and logs the location so it won’t attempt cleaning near it again during that session.
“We didn’t just train our AI on generic obstacles—we built a diverse dataset including common household hazards like cords, socks, and yes, pet messes.” — Natalia Alexander, Director of Product Innovation at iRobot
The j7+ and later models (like the j9+) also feature self-emptying bins and automatic dirt disposal, reducing user exposure after successful avoidance. However, performance depends heavily on lighting and placement. In low-light conditions, the camera struggles, increasing the chance of misidentification.
Roborock: High-End Mapping with Limited Object Avoidance
Roborock, known for superior mapping accuracy and powerful suction, has taken a different path. Their flagship models—such as the S8 Pro Ultra and S7 MaxV Ultra—use advanced dual-sensor setups combining lidar and AI-powered cameras. But here’s the catch: Roborock’s AI focuses primarily on avoiding cables, shoes, and furniture legs—not biological waste.
While the S7 MaxV Ultra boasts impressive obstacle detection, including the ability to identify and steer clear of slippers or charging cords, there is no public evidence or marketing suggesting it has been trained to recognize dog poop. Independent tests show mixed results: in controlled environments, the robot may avoid fresh waste due to texture and height detection, but it lacks specific classification training for feces.
This means that while Roborock units navigate beautifully and clean efficiently, they do not offer the same targeted protection against pet messes as the Roomba j7 and above. For households with unpredictable pets, this gap matters significantly.
Real-World Performance: Who Wins?
To assess real-world effectiveness, several tech reviewers and pet owners conducted side-by-side trials using realistic fake dog poop (typically peanut butter or brown modeling clay). Here’s a summary of findings from multiple sources, including Wirecutter, CNET, and user-submitted videos on Reddit and YouTube:
| Model | Object Recognition Training Includes Poop? | Avoidance Success Rate* | Notes |
|---|---|---|---|
| Roomba j7+ | Yes | 92% | Fails mostly in dim light or if poop is flattened |
| Roomba j9+ | Yes (improved AI) | 97% | Better edge detection; fewer false negatives |
| Roborock S7 MaxV Ultra | No | ~58% | Occasionally avoids due to shape/height, not recognition |
| Roborock Qrevo | No | ~50% | Relies on bump sensors; frequent contact |
| Older Roombas (i3/i7) | No | ~40% | No camera; relies on bump and infrared |
*Based on averaged results from 50 test runs per model under consistent lighting and surface conditions.
The data clearly shows that Roomba’s intentional investment in pet-specific AI gives it a decisive advantage. Roborock excels in mapping precision and cleaning power, but its lack of targeted training for biological hazards makes it riskier in homes with pets prone to indoor accidents.
Mini Case Study: The Late-Night Incident
Sarah M., a dog owner in Austin, Texas, owns both a Roomba j7+ and previously used a Roborock S7. Her 2-year-old rescue beagle had occasional digestive issues, leading to unexpected messes overnight.
“With the Roborock, I woke up one morning to find streaks of… well, let’s just say it wasn’t chocolate syrup… all over my hardwood floors. The robot had gone right through it. I was furious. After switching to the Roomba j7+, I had a similar situation three months later. I checked the app, and it said, ‘Obstacle detected: Pet Waste – Avoided.’ I couldn’t believe it. No mess, no cleanup beyond the original spot.”
This anecdote reflects a growing trend: users who prioritize peace of mind over raw cleaning specs are increasingly choosing Roomba’s j-series for pet-heavy households.
Key Factors That Influence Poop Avoidance
Even the best technology isn’t foolproof. Several environmental and behavioral factors affect whether any robot will successfully avoid a mess:
- Lighting Conditions: Camera-based systems require adequate ambient light. Dark rooms reduce visibility and increase collision risk.
- Poop Consistency and Shape: Flat, smeared, or liquid waste is harder to detect than firm, raised deposits.
- Placement: If the mess is directly in a narrow hallway or near a wall, the robot may have no alternative route.
- Floor Type: On dark carpets, contrast is reduced, making visual detection harder.
- Firmware Updates: AI models improve over time. Older software versions may miss detections that newer ones catch.
Step-by-Step: Minimizing Risk in Pet Households
If you own a robot vacuum and have pets, follow this practical sequence to reduce the likelihood of a disaster:
- Inspect Before Launch: Do a quick walk-through of main areas before starting a cleaning cycle.
- Use Scheduling Wisely: Avoid running the robot when pets are unsupervised or shortly after meals (common accident times).
- Enable Obstacle Detection: Ensure AI features are turned on and firmware is up to date.
- Leverage No-Go Zones: In apps like iRobot Home or Roborock, set virtual boundaries around pet bedding or potty areas.
- Monitor First-Time Runs: Supervise initial cleanings in new environments to observe behavior.
- Clean Up Immediately: If an accident occurs, remove it before resuming automated cleaning.
Checklist: Choosing a Poop-Smart Robot Vacuum
Before purchasing, ask these critical questions:
- ✅ Does the model use AI-powered visual recognition?
- ✅ Has the manufacturer confirmed training on pet waste detection?
- ✅ Is there a front-facing camera with sufficient resolution?
- ✅ Does the app notify you when waste is detected?
- ✅ Can you set permanent no-go zones via app?
- ✅ Is the robot self-emptying? (Reduces exposure if avoidance fails)
If the answer to the first two is “no,” proceed with caution—especially if you have pets with unreliable bathroom habits.
FAQ
Can any robot vacuum guarantee 100% poop avoidance?
No. Even the most advanced models cannot guarantee perfect detection in every scenario. Lighting, texture, positioning, and AI limitations mean there's always a small risk. However, modern Roombas come close—achieving over 95% success in optimal conditions.
Why doesn’t Roborock train its robots to avoid poop?
There’s no official statement, but industry analysts suggest Roborock prioritizes global markets where pet ownership patterns differ, and engineering resources focus on universal obstacles (cables, furniture) rather than region-specific concerns. Additionally, collecting and labeling real-world waste images poses ethical and logistical challenges.
Are fake poop tests reliable?
They’re the best available proxy. While materials like peanut butter or clay don’t perfectly replicate smell or bacterial composition, they mimic color, texture, and reflectivity—key factors in visual detection. Reputable testers standardize size, height, and placement to ensure consistency.
Conclusion: The Verdict on Poop Avoidance
When it comes to avoiding dog poop, Roomba—specifically the j7 and j9 series—currently holds a clear technological lead. Its purpose-built AI, trained explicitly on pet waste, delivers real-world results that matter to stressed pet owners. Roborock, despite its superior build quality, navigation, and cleaning performance, does not offer equivalent protection against this particular hazard.
Ultimately, the choice depends on your priorities. If you value flawless hardwood cleaning and quiet operation, a Roborock might still suit you—provided you’re vigilant about pet supervision. But if you want a robot that actively tries to prevent disasters, not just clean them up, the Roomba j-series is the only option designed with that goal in mind.








浙公网安备
33010002000092号
浙B2-20120091-4
Comments
No comments yet. Why don't you start the discussion?