Hands-on Gear Review

iRobot Roomba 690 Review

Price:   $375 List | $299.00 at Amazon
Pros:  Relatively inexpensive, surprisingly proficient at navigating
Cons:  Semi-random cleaning pattern, unimpressive at cleaning hard floors or picking up pet hair
Bottom line:  This bare-bones Roomba model is a little on the pricey side for its performance and overall is quite lackluster in performance
Editors' Rating:   
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Bin Capacity:  .6L
Dimensions:  13" diameter, 3.7" high
Measured Weight:  7.6 lbs
Manufacturer:   iRobot

Our Verdict

Overall, we felt that the Roomba 690 somewhat fell through the cracks compared to the rest of the group. It is a little too pricey for its poor performance for us to recommend it as a value option, as there are other vacuums that perform essentially identically and cost about $100 less and it doesn't perform well enough to even come close to the top products. It navigates obstacles surprisingly well, but doesn't necessarily cover the majority of your home due to its rather erratic driving pattern. It also doesn't really do the best job cleaning hard floors and is absolutely terrible at collecting pet hair. Needless to say, it isn't our favorite by any means.

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Our Analysis and Test Results

Review by:
David Wise and Austin Palmer

Last Updated:
June 13, 2018

The 690 finished a ways behind the Samsung POWERbot R7070 and just ahead of the Ecovacs Deebot N79S, matching the performance of the Shark ION Robot 750. The Ecovacs, Shark, and Roomba 690 all use a semi-random navigation method and performed about the same, doing a solid job at maneuvering around obstacles but missing large swaths of your floor, especially when cleaning multiple rooms. The Samsung uses a systematic cleaning method, but executed it very poorly in our test, putting it below the semi-random models in navigation, but it performed much better when it comes to raw cleaning power.

Performance Comparison

We bought all the best robot vacuums on the market in our quest to find the top product, testing them all head-to-head to find out which one really is the sweeper supreme. We have spent over two years reviewing these products and refining and revising our testing methods, so you can buy with confidence, knowing you are getting the perfect robot vacuum to match your needs and budgets. We divided up our testing process into six weighted rating metrics, with the results of the Roomba 690 described below.


By far the biggest component of the overall score at 30%, strong navigational skills are a key component for any top robot vacuum. We constructed an entire room's worth of simulated furniture, then set each robot lose and scored how well each one did at navigating the room while cleaning. We also timed how long it took for each robot to find its charging base and tested how its sensor reacted to driving under low furniture and over floors that have high-contrast transitions. The Roomba 690 performed above average, earning a 6 out of 10 for its results.

The 690 did surprisingly well at maneuvering around the various pieces of test furniture. While it was very hit-or-miss where it went in the room, it was usually very adept at escaping from underneath furniture when it had finished cleaning there — much faster than some of the other semi-random robots.

This robot also did quite well at driving under low pieces of furniture, essentially making it under anything that the robot actually fit.

However, the 690 did very poorly in the remaining tests in this metric. This model took the longest by far to find its docking station, basically bouncing around until it was directly in front of it before driving towards it with any sort of purpose.

It also wouldn't drove over darker areas of the floor if there was a sharp contrast, thinking that it was a drop off as the edge detection sensors would trigger.

Home Coverage

Differing slightly from the prior metric, we judged each robot on how much of your home it actually cleaned for this test, instead of if it simply managing to avoid becoming trapped. Additionally, we also scored each robot on its spot cleaning function, whether or not it could handle cleaning multiple rooms and how effective the barrier system on each robot is. Altogether, these evaluations account for 20% of the total score, with the Roomba 960 delivering a lackluster showing and earning a 4 out of 10 for its efforts.

This robot definitely missed large sections of our furnished test room, though it did get closer to the furniture items than some of the other semi-random robots did.

This robot does have a decent spot clean function, covering an area a little over 15 sq. ft.

We also liked the barrier system available on the 690, which used the same virtual wall beacon as the other Roomba models, allowing you to create a linear virtual wall about 10' in length or a circular one about 4' in diameter.

Unfortunately, this model isn't really suited for cleaning multiple rooms and will not automatically recharge and resume cleaning if the battery becomes depleted.

Carpet Cleaning

Next, we evaluated the cleaning power of the Roomba 690, starting off with its performance on soft floors. We tested how well it dealt with the corners and edges of a room, as well as its proficiency at picking up small, medium, and large particles, using both low-pile and medium-pile carpet for the bulk of our tests. These tests account for 15% of the total score, with the Roomba 690 doing reasonably well, earning a 6 out of 10 for its efforts.

This product didn't get off to the greatest start, performing somewhat poorly in our corners and edges test. We ran each robot in our testing pen, then measured how closely it cleaned along the edges and corners. It left a strip about 4" in width of uncollected debris along the straight edges and a triangle measuring a little over 6" at its apex in the corners.

The performance of this product improved slightly in our flour collection test, doing about average. It collected a respectable amount of flour, only leaving a small amount behind.

The 690 improved dramatically in our rice collection test, collecting the vast majority of the rice and leaving less than ⅛ of a teaspoon behind on both the low-pile, flat carpet and the medium-pile, fluffier carpet. For the oat cleanup test, the 690 delivered one of the best showings of the entire group, collecting all of the oats from the flat carpet and only missing about 3 oats on the fluffier carpet.

This robot finished out with a strong showing in the final test of this metric, collecting all six of the mini-wheats from both types of carpet.

Hard Surface Cleaning

This metric is also responsible for 15% of the total score and is comprised of the same set of tests that we conducted on soft floors, though this time we replaced the low and medium-pile carpet with a section of hardwood laminate floor. Unfortunately, the 690 failed to impress earning a 4 out of 10 for its substandard showing.

The 690 did slightly worse on our edges and corners test on the hard floor, leaving a line of leftover debris about 4.5" across. It also left plenty of squeegee marks as it tracked and pushed around the flour, rather than collecting it.

This robot continued its lackluster performance into our flour collection test, leaving behind plenty of flour and tracking it around, rather than successfully sucking it up into the collection bin.

Moving on to our rice clean up challenge, the performance of the 690 improved dramatically. While it did fling some rice aside with its rotating brush, it eventually got almost all of it, only leaving two or three grains behind. It did a little worse with the oats, tending to fling them around much more frequently and further away, causing it to miss cleaning some up.

The 690 also fared poorly at picking up mini-wheats, failing to collect any and only pushing them around instead.

Pet Hair

Responsible for 10% of the final, our Pet Hair metric is the last of our cleaning assessments for these products. We spread out a preset amount of pet hair that we collected from a local groomer on both types of test carpet, then scored each product on how much it sucked up. The Roomba 690 was a bit of a disappointment, earning a 2 out of 10 for its overall performance.

For the fluffier carpet, the 690 got about 14% of the hair we laid out, with most of it making it all the way into the collection bin, rather than becoming tangled in the main brush. It did a little worse on the flat carpet, only collecting about 6% of the hair, with the best models getting 35-40% of the hair in both tests.

Smart Connect

Lastly, we evaluated and scored the networked smart capabilities or other connected features of these products for the remaining 10% of the total score. The Roomba 690 did all right, earning a 6 out of 10 for its results.

We found the 690 to be quite reliable at staying connected to the network, never really requiring us to reset it or wipe the connection and start over. The mobile companion app for this robot is about average in terms of functionality, letting you locate the robot, read helpful hints, find the robot, and track the life of the side brush, main brush, and filter, as well as set a schedule and start it cleaning.

This robot can also interface with existing smart home systems, such as Google Home, IFTTT, or Amazon Alexa to name a few. You can set a 7-day cleaning schedule for the robot on the mobile app, but not on the device itself. However, you can start it cleaning your home, send it home, or start a spot clean cycle from different buttons right on the robot, if you don't feel like using your smartphone.


We didn't find the Roomba 690 to be a great value option, as there are significantly less expensive options that only performed slightly worse.


All in all, we would find it hard to recommend theRoomba 690. If you are shopping on a tight budget, then there are less expensive models that you should consider or if you are willing to pay a little bit more, it is well worth it to spend a bit more and get a model that is more in the $500 rather than getting the 690. There are some models in that range that are far better at navigating your home and cleaning than the 690 and will end up being much more useful in the long run.
David Wise and Austin Palmer

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