Hardware in the Loop: Training Robot Contact in an Unstructured Environment


This talk presents the story and software architecture behind an experimentally tested, machine learning framework for robot contact classification and motion control using a KUKA LBR iiwa robot, gRPC, and Python.

Apr 19, 2021 6:00 PM


  • Project Director @ Halodi Robotics, running our worldwide projects and building the North American HQ in Montréal
  • Moonlight as a mentor for Techstars and FounderFuel
  • Ph.D. at ÉTS focusing on collaborative robotics + machine learning, and mechanical + biomedical engineering at McGill
  • Typically find me at the interface of hardware and software.

  • I’m fortunate to have the opportunity to work at Halodi Robotics and work on collaborative robotics
  • We want to bring robots into the human world instead of changing the world for robots
  • This is very different from the typical approach in industrial robotics, where entire factories are built around just the robots, not the humans
  • We have the dream of creating helper robots for home and healthcare, but that’s a challenging application
  • So we’re taking things step-by-step and are starting with “simpler” applications in the human world, such as security guarding, retail, and food packaging
  • But while robotics is full of complex problems, let’s focus on my favourite: physical human-robot interaction

  • How do we make physical human-robot interaction safe?
  • How do we make it safe?
  • How do we ensure that when robots collide, contact, and crash into humans, that it’s safe?
  • Well, that’s what I tried to solve during my Ph.D., and this guiding question led me to create some weird systems.

  • The original idea of my research was to perform freehand medical ultrasound on human limbs using collaborative robots
  • But I soon realized that just the physical interaction between a probe and the human body was a fascinating problem that needed more attention

  • Pure motion control is not good enough, and tuning the controller and all the parameters for physical human-robot interaction is not trivial
  • And every body part or object with different stiffnesses and properties would need its controller tuning

  • Then I also thought, “wait, this is an unstructured environment; how does the robot know what it’s in contact with?”
  • Can I also train a model to tell the difference between a leg and a table just by touch? How about the difference between a calf, knee, and ankle?

  • So for a typical machine learning and optimization approach, I would collect a lot of training data, apply it to a model, get some output, update the model, and repeat

  • But where do I put the robot and the actual hardware in this process?

  • And how should I implement communication between my Python data science stack and the Java-based controller that the robot uses?

  • Well, to solve the communications issue, gRPC was the answer
  • It allowed me to create a client-server model between the robot and my python stack
  • The robot was a “server” in the eyes of the data models and training algorithms
  • The function callbacks from the optimization loops would call blocking server functions on the robot, which would make the robot move or do something physical
  • The return value was the data collected from the robot’s onboard sensors

  • For those not familiar with gRPC, we define our service using a language-neutral proto file that can auto-generate boilerplate code in basically every language.
  • Python and Java in this case
  • It’s a lot of fun because it integrates nicely into CI/CD. All my services and codebases can use the same core proto definition, and their interfaces can stay in sync.
  • First, we define a service that contains a set of functions we’d like to call from our client.
  • In this example, we have a “RobotService” that has a “Move” command.
  • It requires an array of joints as an input and returns a SessionResult message object.
  • Those message objects are then defined below.

  • On the python side, we define a class for our client with the service stub boilerplate

  • for the actual training and optimization, we can call the robot as if it were a simple function provider
  • we connect to the robot through our controller client class and call services whenever we need
  • here’s an example of using Scipy’s differential optimization with a simple callback objective function that has the robot run a motion session
  • The result of that session may contain a bunch of data from the robot that we then evaluate to obtain a single float value that we’re trying to minimize

  • And here’s where this particular example was used in real life.
  • The human body is a deformable surface and an unstructured environment, a safety concern and a challenge for trajectory planning and control.
  • I was trying to optimize for the smoothest, bounce-free motion along a limb while performing ultrasound.

  • to tune the parameters, differential evolution was used with the robot in the loop
  • each time the “evaluate fitness” step is run, that’s the actual robot with the Java controller being called from the scipy optimization function through gRPC to run a session with a given vector of motion settings
  • it responds to the python data processing client with a SessionResult filled with force sensor data that then is converted to a single float measure of fitness or quality

  • Through real-world sessions with a collaborative robot, the framework tuned motion control for optimal and safe trajectories along the human leg phantom.
  • Hundreds of sessions were performed, and generations of candidate solutions were developed

  • And here we have the result of this particular experiment: smooth motion

  • So from an experimental design perspective, the big win with something like this is that I can set it and forget it, letting it collect all the data and run its own sessions on its own
  • This leads me to one of the simplest and silliest experiments I ever designed…

  • Guess what I did here…

  • Yup, I had the robot learn to poke
  • Robotic medical ultrasound is an example of a task where simply stopping the robot upon contact detection may not be an appropriate reaction strategy.
  • The robot should have an awareness of body contact location to plan force-controlled trajectories along the human body properly
  • So Here I made a framework for robot contact classification using robot force sensor data

  • We wanted to build a classifier model that answered the question: “What was involved in the contact event?”

  • To gather the data to train the classifier, the robot was programmed to poke until a force condition was triggered with several different scenario types

  • Built on the same communications architecture where the python data science and machine learning stack treated the robot as a server with callable functions through gRPC
  • Once again, the big win with something like this is that I can set it and forget it, letting it collect the training data for me
  • I can also quickly test my code by mocking the robot “server” from the client’s perspective
  • This allows for a beautiful separation of concerns and a more robust codebase

  • Back to the application… On the machine learning side, We turned to scikit-learn for a simple, quick, and effective pipeline
  • With just half a second of single-axis force data, some preprocessing steps, and a decision tree classifier, we were able to have the robot know WHAT was involved in the contact event, not just that a collision occurred

  • The code is quite simple too
  • Scikit-learn makes it trivial to create and train pipelines, even branching pipelines with multiple preprocessing steps
  • This lets us test our hypotheses quickly and effectively

  • Beyond machine learning applications, I’ve used this python data stack connecting through gRPC to hardware for a variety of applications, including robot calibration with laser systems
  • The FARO laser was also controlled with gRPC

  • And this is why I love this architecture for bringing hardware into the loop: it’s scalable and testable
  • All these hardware devices become abstract servers that provide a set of callable functions to a client
  • The client doesn’t need to know what they are or how they’re implemented, which makes it great for testing and mocking
  • The hardware controllers don’t need to know anything about machine learning, data science, or application stuff; they focus on what they need to do to execute the function properly
  • All my interfaces can stay versioned and in sync with autogenerated boilerplate and proto files
  • And from a hardware engineering perspective, my experiment design and setup time is significantly reduced through automation and a well-defined interface
Nicholas Nadeau, Ph.D., P.Eng.
Nicholas Nadeau, Ph.D., P.Eng.
Founder / Fractional CTO

Nicholas Nadeau is a fractional CTO empowering startups with next-gen technology expertise, and a passion for driving corporate innovation. Stay informed on cutting-edge hard tech trends - subscribe to my newsletter. Ready to innovate? Discover my services and accelerate your growth.