Impact

Founded in 2020 to democratize last-mile delivery, Coco currently operates its sidewalk robots in Santa Monica and West Los Angeles for food delivery. Their long-term vision is to deploy an autonomous fleet tackling a wide array of delivery verticals – all without contributing to urban congestion or pollution.

As Coco scaled, resolving incidents became a major bottleneck. A single issue started taking hours of engineers' time. Eventually, the team realized that to achieve their goals, they needed better robotics observability – a faster and more scalable approach to capturing, organizing, and learning from their data.

By adopting Foxglove, Coco learned to make sense of their complex data quickly and reduced incident resolution time from hours to seconds.

Challenges

Juggling different data across disparate tools

Because Coco’s systems dealt with heterogeneous data streams, the hardware, software, and quality teams used a variety of tools to collaborate on their data. While these solutions worked great individually, they didn’t integrate well with each other. Analyzing, sharing, and cross-referencing data across these tools became an increasingly clunky process that cost hours of developer time daily.

This patchwork system made it difficult for Coco’s engineers to collaborate. Sensor data, log messages, control signals, and videos were recorded in different formats across various frameworks, forcing engineers to jump between as many as five tools to analyze a trip. In fact, they rarely looked at the robots’ ROS bag files, since they contained such a minor subset of the full picture.

Bottlenecking progress with manual workflows

To view any recording of a robot’s trip, Coco engineers first had to transcode the data, select a time range to explore, write a script to transform it to the correct format in a Jupyter Notebook, wait several minutes for the output MP4 file, and then finally view it in a web browser.

This process was long, error-prone, and not scalable. Few teammates were technical enough to use all the tools, so teamwide progress often depended on these engineers’ availability. In fact, Coco routinely missed opportunities to properly resolve incidents, since the barrier to inspecting them was so high.

Training and auditing human pilots

Training and auditing Coco’s pilots required a large investment of time and resources.

For new pilots, an experienced teleoperator had to physically be in the room to watch how they
handled deliveries. Even for more experienced pilots, Coco found it difficult to audit whether
they were reporting all incidents (e.g. a robot flip, a pedestrian interacting with the robot,
etc.) accurately. To review a session, an engineer had to reference multiple databases to pull
the relevant video for a given robot and timeframe, playback hours of footage to spot-check a
few incidents, then reference another database to figure out which pilot was responsible for the
incidents.

Deploying Foxglove to accelerate development

With the Foxglove platform, Coco knew they could tackle most of their challenges with one software solution. They were excited to get support across their entire development process – from data storage and management to visualization and analysis.

Bringing multimodal data into one integrated environment

Whether recording data on the robot, from the pilots’ workstations, or via its autonomy stack, Coco needed to pull its multimodal data streams together in one place for efficient analysis.

To accomplish this, Coco leveraged MCAP, a container file format developed
by Foxglove, to merge their heterogeneous data into a common log format. They imported their
newly consolidated files into Foxglove Data Platform for
easy team-wide collaboration. With Data Platform’s intuitive web interface, Coco engineers can
now reference a central repository to annotate, organize, and analyze data. Clicking on a
recording instantly takes them to Foxglove Studio, where they can
scrub back and forth to jump to the timestamps they care about most. Here, they can compose rich
layouts that visualize everything from camera feed
images to log
messages and 3D
markers.

With this migration, Coco now stores, visualizes, and debugs data in one integrated development
environment, without jumping between software solutions or handing off tasks between teammates.
This bird’s-eye view has made their issue tracking more efficient than ever – analysis tasks
have gone from taking hours to seconds, and the number of developer tools used has decreased
fivefold.

Democratizing team access to data and insights

When taking robots on test drives or deliveries, Coco team members can now access the recordings in Foxglove Data Platform by the time they bring the robots back inside. They no longer have to wait for an available engineer to query various databases and use multiple tools to download video footage. Any technical or non-technical team member can spend a few seconds clicking around on the Data Platform timeline to find the footage they’re interested in, along with all its associated metadata (e.g. recording robot, delivery trip details, map, etc.).

By integrating Data Platform’s tagging infrastructure with Foxglove events, Coco has also been
able to batch-review incidents for more streamlined triaging. Whether it’s a pilot adding a
tag for a robot-human interaction or a script automatically detecting issues like robot flips,
the Coco team can step through a list of these events within minutes to assess whether further
analysis is necessary. Not only would it facilitate analysis in the short-term, but the benefits
of having organized data would continue growing as Coco collected more data into the future.

It was possible to look across a hundred trips, quickly locate all instances of an issue, and
summarize its impact on the robots’ performance within minutes.

Tagging data with events has also helped Coco more easily follow up on deeper analysis. Engineers can now quickly review all instances of a failure mode and make decisions based on that information, instead of spending hours or days wading through petabytes of unstructured data.

Improving human pilot oversight and training

Better data visibility has empowered Coco engineers to better collaborate with human pilots. Previously when pilots manually reported incidents, their subjective judgment calls often resulted in inconsistent records. But with automatic tagging and easier access to video footage, engineers can now easily cross-reference generated events against pilots’ reports to audit them for accuracy. Instead of reviewing hours of footage to audit trip reports, they can now scan the main points of interest in seconds and use Foxglove Studio to get more qualitative context on any discrepancies.

Outcome

Before Foxglove, Coco often made strategic business decisions based on assumptions or approximations – largely because it was so time-consuming to compile the data needed to make informed decisions. Once Foxglove placed the data at Coco’s fingertips, it exposed parts of the business that the team hadn’t been seeing. Coco can now access data, triage incidents, and weigh the impact of an issue on their business in seconds. Their engineers can also now collect cleaner data around their key metrics – data that will power future iterations of their autonomy software – and make the necessary adjustments to their roadmaps.

With this integration, Foxglove has become a one-stop solution for Coco’s development workflows. The platform is now pervasive across the company – the Trust and Safety team use it to review incidents, pilots to log their trips, and engineers to share collaboration links. By helping them tackle common development tasks, Foxglove has allowed Coco to avoid tooling debates and focus instead on building high-performance robots.


Impact

Tangram Vision helps robotics teams building sensor-enabled products scale their perception stack – from one proof-of-concept robot to an entire fleet. Whether you’re adding new sensing modalities, layering on perception algorithms, or simply trying to get your cameras and lidars to communicate with each other, Tangram’s software takes these esoteric tasks off of your engineers’ to-do lists and magically makes all of it work.

But over the last year, Tangram began to realize that it was taking too long to get their software up and running at customer sites. To assess customers' data, Tangram engineers had to visit their robots in person, bring the data back to their offices, and convert it into a supported format to finally inspect it.

In short, Tangram lacked a unified solution for robotics observability. By adopting Foxglove and the MCAP file format, Tangram was able to slash their onboarding workflows from months to days.

Challenges

Spending weeks to demonstrate value to customers

When engaging with a new customer, Tangram Vision takes them through a pilot process to demonstrate the value of integrating with their software. Before adopting Tangram, customers typically want to first see it adding concrete value to their development efforts.

Before integrating with Foxglove, this typically meant traveling on-site to the customer to collect sample data off a physical robot. Tangram engineers then offloaded this data, took it back to their offices, converted it into a supported format, and inspected it with their internal data visualization tooling. At this point, they could discover critical issues – parts of the data could be corrupt, unusable, or missing altogether. They would then work with the customer to fix these issues – by either scheduling time to come back on-site or guiding the customer’s engineers through sending another data set. Once they finally have “good data” to work with, Tangram can process it with their software to assess the customer's existing perception processes and provide actionable recommendations for leveling up their system.

Every time we moved data from point A to point B, we encountered an extreme amount of friction.
When your value proposition is saving customers months of work, but your own process takes weeks
to establish, it's not good value.

This entire process could easily take weeks to complete, depending on the state of customers' data. Not catching issues early often introduced days to weeks of delays. Other unforeseen issues – like the designated robot now being busy for a week, out of commission due to hardware issues, or partially disassembled for maintenance – could make the back and forth needed to get this sample dataset even longer.

Hard-to-scale consultative work

Currently, robotics development is a very fractured ecosystem, and no two robotics stacks are alike. For Tangram Vision, every individual integration meant getting to know a new stack with its own unique requirements. This made onboarding new customers incredibly manual, as each one required an unsustainable amount of time and assistance for configuration and validation. Not only did this stretch out the time needed to convert a pilot user into a paying customer, it also limited the number of new customers Tangram could take on at any given time.

Instead of operating as a software company, we were falling into the trap of providing consulting
services – which was neither the plan nor a scalable strategy.

Tangram Visions’s customers also recorded their data in a wide range of serialization formats, further contributing to onboarding friction. When accepting and juggling these different formats for validation, Tangram had to invent ways to organize each customer’s files – with methods as basic as using different Google Drive folders for different types of sensor data. Even after collecting the right data, Tangram then had to consult closely with the customer’s engineers to integrate that unique data with their software. All this unstandardized data collection made it difficult to collect accurate, complete, and synced customer data both quickly and repeatedly.

Wasting resources on undifferentiated tooling

Tangram Vision developed their own data visualization tooling in-house to help them understand customer data. While the tool initially served their purposes, it quickly became clear that building robust visualization software was neither the team’s interest nor specialty.

As the customer base grew and development work scaled, it became increasingly difficult to maintain this visualization code. Tangram engineers struggled to keep pace with new and changing requirements, especially while serving customers that weren't using a unified format for their data. Whenever Tangram changed anything in their perception software, engineers would then have to spend a day tending to the newly broken visualization features downstream and updating rendering logic to adapt to the changes.

Adopting Foxglove to streamline processes

More efficient customer onboarding with MCAP

Tangram Vision adopted MCAP as their preferred file format for collaborating on data – both internally and with customers. This has provided all parties with a common data language and some much-needed consistency, making it infinitely easier for Tangram to support their customers.

By collecting and evaluating data in one standard format, Tangram Vision can now understand the state of their customers’ processes – and demonstrate the worth of their software – in record time. Instead of taking each customer's uniquely formatted data files back to their workstations for bespoke conversion, deciphering, and analysis, they can feed all of it through the same process. Even for new customers that haven’t already made the transition to MCAP, they can easily point them to the provided CLI tool to convert their data files with a few simple commands.

We can now provide a path forward for customers – and prove our worth to them – within 24 hours.
They can immediately understand the impact of working with us. This time last year, that same
process took weeks. Now, we're getting it down to minutes.

Recording in MCAP has also resulted in much more lightweight files, making it easier than ever to ingest data off the robot, send files back and forth, and load them for validation. Instead of waiting days or weeks to inspect a customer’s data, Tangram engineers can now offload the robot's newly recorded MCAP files, load them directly into Foxglove for visualization, and immediately surface any critical issues – all while they’re still on-site with the customer.

Increased bandwidth to take on new customers

By adopting MCAP and Foxglove, Tangram Vision has been able to take stock of critical friction points in their customer onboarding and streamline each of them, one by one.

Instead of having to juggle different data formats, maintain in-house visualization tooling, or provide customer-specific integrations, Tangram can now rely on the Foxglove platform to do most of the heavy lifting. Every step that required Tangram's consultative knowledge has been standardized and simplified, so that they can spend less time holding their customers' hands and more time building cutting-edge perception infrastructure.

Adopting Foxglove and MCAP has freed up more of our time and empowered us to take on a higher
volume of customers.

The Foxglove integration has also unlocked new sales opportunities for Tangram Vision. Since they no longer have to provide as much consulting, the entire customer onboarding process has become much more self-serve and sustainable than ever, meaning more bandwidth to pursue new leads and onboard more users.

Extending out-of-the-box visualizations with powerful customizations

Instead of spending their own resources building and maintaining a homegrown data visualization tool, Tangram Vision now relies on Foxglove's expertise to explore and analyze their customers’ data.

Foxglove layout

Thanks to Foxglove's extensibility, the team has been able to build on top of the general-purpose feature set to address their perception-specific needs. Some engineers have authored custom data exploration panels and arranged them into a shareable layout to visualize their customers’ calibration results. Even though these features did not come out-of-the-box, building these modular extensions were a relatively straightforward process that required much less work than building an entire visualization suite from scratch. Tangram engineers have been able to apply this one summary view across a wide range of customers, sensors, and robotics applications, further streamlining their debugging and customer onboarding workflows.

Outcome

By adopting MCAP and Foxglove, the Tangram Vision team has been able to cut entire weeks out of their pilot onboarding process. They can now get concrete results for their customers within minutes of receiving their data, subsequently extend the proof of concept to days of deployment, and then set their sights on engaging with the next set of customers.

With this streamlined data collection and customer collaboration process, Tangram Vision no longer has to waste months of precious time performing repetitive data cleaning tasks or navigating each customer’s unique setup. Instead, their team can focus their efforts wholly on their actual mission – building perception tooling that helps robotics teams manage the growing complexity of their sensor systems.


Capabilities unlocked

Self-driving companies have historically approached autonomy software development with hand-crafted and hand-tuned algorithms, but Waabi is trying something different. They’re taking a much more scalable approach by harnessing AI to power all aspects of their development. They’ve built a groundbreaking AI-based simulation system and virtual driver for autonomous trucking operations – one that can generalize much better across different real-world scenarios.

As Waabi began its self-driving journey, the team recognized the need to navigate dense data, organize test results, and resolve issues quickly. While laying the proper groundwork would accelerate all future development, Waabi knew that building a complete robotics observability platform would be a significant investment of time and resources.

By adopting Foxglove and the MCAP file format, Waabi was able to hit the ground running. The team took advantage of Foxglove's existing foundation to accelerate their development and testing processes and to streamline collaboration.

Adopting Foxglove

Understanding their data to get to market faster

By being a “late mover”, Waabi has witnessed firsthand how other robotics teams have operated – and often failed. Historically, robotics companies have each built bespoke versions of similar tools to organize data, triage incidents, and debug issues. But creating a world-class developer tool suite that would meaningfully accelerate work could easily take years – from staffing a full-time internal team to building out a workable MVP and maintaining the software with bug fixes and new features. These autonomous driving companies were burning valuable resources on duplicated work that didn't align with their core competencies or ultimate goal.

As Waabi began developing their virtual driver software, they wanted to avoid making the same mistake. They integrated Foxglove to access tooling that would work out-of-the-box and with minimal setup. Instead of reproducing Foxglove features in-house, Waabi unblocked their engineers right away and let them hit the ground running doing what they do best – applying their cutting-edge AI and ML research to build autonomy and simulation software.

We knew we wanted to leverage off-the-shelf developer tooling where possible – especially because
we saw how costly it had been for other companies to reinvent the wheel. By adopting Foxglove, we
were able to focus on our unique differentiators.

Waabi trucks on the road

Recording drive data in a common format

Waabi adopted MCAP, Foxglove’s open-source file format, as a unified data format to represent their driving data. They knew they wanted an efficient append-only file format to avoid on-vehicle overhead, but also wanted time-indexing and topic partitioning to retrieve arbitrary subsets of the data on demand. Paired with Foxglove, the MCAP format helped Waabi realize these goals without their engineers having to create custom formats and data pipelines.

With MCAP, the team had a common data language that they were speaking. Instead of having to worry about different subsets of their multimodal data being recorded in different formats, they could store these files in the same place, organize them in a consistent way, and inspect them with the same suite of tools. Instead of wasting time on converting data into different formats or switching between tools for various tasks, everyone on the team could simply focus on uploading data, debugging issues, and improving their autonomy software.

Streamlining cross-team collaboration

While Waabi first started using Foxglove to streamline development for its autonomy engineers, numerous teams across the company now use the platform for a wide variety of tasks – including inspecting driving logs, identifying key scenes for triage, and visualizing data while the system is on the road.

Waabi engineers have also been annotating their data with Foxglove events to identify interesting scenarios robots encounter on the road. By evaluating metadata across multiple events, Waabi can easily categorize them into relevant buckets for further analysis. This tagging feature has helped the team augment their simulation scenarios test suite and improve their autonomy models.

Composing rich debugging and visualization workspaces in Foxglove, and sharing them with team members, has also accelerated Waabi’s root cause processes.These Foxglove layouts make it easy to build a collection of ready-to-go dashboards for the whole company to use. Sharing a snapshot of a workspace for collaborating with another team member has now become as simple as copying and pasting a link.

Outcome

Foxglove has proven to be a critical part of Waabi’s development toolchain. It has allowed the Waabi
team to stay focused on building the innovative AI-powered software for its virtual driver and simulation
platform, while having a solid visualization and data indexing platform supporting it.

Foxglove has enabled
Waabi to better organize its driving data, allowing the team to inspect and tag that data, visualize
both on-the-road and simulated behaviors, and debug autonomous driving performance. These features have
helped Waabi to better visualize how well their latest features work, and use those findings to prioritize
their development roadmap as they rapidly make progress in bringing safe and scalable self-driving truck
technology to the road.


Impact

Yard Robotics is on a mission to automate lawn care – to help people reclaim time for themselves, their families, and what matters to them most. As a replacement for traditional mowing and turf maintenance services, Yard Robotics deploys custom bots to map and traverse homeowners’ properties, leaving behind impeccably mown lawns and delighted customers.

The Yard team relies heavily on Foxglove's observability platform to understand and learn from their data. By integrating Foxglove with almost every core workflow, Yard has been able to scale their operations, accelerate their debugging workflows, and compress their time-to-market journey by nearly 6 months.

Yard bots mapping yards

Adopting Foxglove

Accelerating autonomy software development with visualizations

While having broad test coverage is certainly critical to any software development, relying solely on manual testing methods to catch issues can be quite tedious.

With Foxglove visualizations, Yard engineers have a powerful shortcut to finding, triaging, and ultimately resolving issues with their autonomy stack. Resolving issues has become as simple as visualizing a bot’s recorded log data, or watching what the bots are doing in real-time.

We’re a small company developing an autonomy stack from scratch. In the same way AI acts a
multiplier for human beings, Foxglove is doing the same for our robotics team. It’s become a huge
and irreplaceable accelerant to our development.

By publishing the relevant 3D visualization markers to denote important information – parallel lines for the track the bots are planning to follow, color-coded pins for different waypoints, gray “keep out” zones, red wheels for overheating motors, green bodies for bots that are ready to engage, etc. – Yard engineers can quickly glance at a 3D panel to understand how their nodes, machine learning models, and bots are all doing. Instead of having to check different parts of the logs, or even different panels in Foxglove Studio, the team can simply look at a 3D scene to get everything they need – significantly speeding up the entire issue resolution and development process.

Maps in Foxglove Studio's 3D panel

The team also relies heavily on the Plot panel, especially for making sense of a high volume of fast incoming data – for tuning PID controllers, tracking odometry values, and watching the control and command signals. Instead of manually reading through log data, Yard roboticists can now rely on these human-friendly representations of their data to quickly solve problems and seamlessly integrate their learnings into the next iteration of their autonomy software.

Understanding our data without Foxglove sounds about as painful as building a robot stack without
ROS.

Reducing onboarding time and improving training

When adopting ROS as their robotics framework, Yard engineers knew this decision could subject new team members to a particularly long and painful onboarding process. Every developer would be restricted to the same Linux platform – regardless of their own experience – and be forced to use notoriously clunky native developer tools like RViz. Onboarding with these tools alone – with their dense interfaces, hyper-technical controls, and various idiosyncrasies – could easily take weeks or months, quickly demotivating developers looking to make a difference on the team.

With Foxglove, Yard roboticists hit the ground running within a few days. Because it is a cross-platform app, engineers can connect Studio to their ROS stack using a Linux, Windows, or macOS machine. Because it has an intuitive interface that doesn’t require any niche domain knowledge to navigate, even engineers without any prior experience can get up-to-speed on common ROS paradigms like topics, messages, and visualization markers.

Less technical team members can also use Studio to make significant contributions to the team. While they may need a teammate to configure a layout for them, they can help triage incidents, remotely monitor deployed bots, and resolve issues once that workspace is set up.

Enabling remote assistance and monitoring

In addition to using Foxglove Studio for visualization and debugging, Yard also uses the app as its primary back-end remote monitoring system.

Yard’s bots communicate over LTE to a back-end that pipes all data into a single ROS master. Remote assistance technicians can then connect to this data source with Foxglove Studio to get a 20,000 feet view of their fleet. They can see all their robots on maps throughout the city and get a sense of how the fleet is doing – where every bot is located, the progress it’s making, and its planned route.

Foxglove Studio as remote assistance

Remote monitoring has enabled Yard to avoid sending technicians on-site to supervise the bots. Those same technicians can now sit in an air-conditioned room with a laptop and remotely handle hardware and software issues as they arise.

Outcome

Integrating Foxglove into their core workflows has streamlined work across Yard’s entire team and dramatically accelerated their progress from prototype to production. It has empowered engineers to focus on their autonomy software, get robots to production, and scale the fleet. It’s made the business of troubleshooting incidents out in the field – and iterating with that feedback – as simple as clicking a few buttons in a Foxglove layout.

Instead of being restricted to a lab somewhere, Yard bots are out in the field every day, mowing more lawns and serving more customers. Their real-world experience is powering the R\&D needed for the next iteration.

Foxglove has been a game-changer for us. Investors are surprised with the progress we’ve made with
relatively few engineering resources, and it's because we are leveraging tools like Foxglove.

With Foxglove, Yard has been able to maintain their breakneck rate of development and keep themselves on track towards their mission. While they’ve started with residential areas as their operational domain, the team is excited to target more complicated tasks and expand their fleet as they iterate on their tech – using Foxglove every step of the way.


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