Sorry for the late reply on this, it’s possible to use NVIDIA’s GR00T-N1-2B model with the AR4 robot, but it requires an integration layer between GR00T’s high-level outputs and the AR4’s low-level motion control.
GR00T is a multimodal AI model designed to control robots through vision and language — it may output task goals, end-effector poses, or even joint targets, but it’s not directly compatible with the AR4’s serial-based control system out of the box.
The AR4 is controlled through a Teensy 4.1 microcontroller and receives joint commands via custom serial strings (e.g., RJ, LJ, GJ commands). To connect GR00T to the AR4, you'd need a middleware layer that:
Interprets GR00T’s outputs (like “pick up the red block” or a desired 6DOF pose)
Converts those into joint angles using inverse kinematics (you can use the AR4’s Python IK function)
Sends joint commands to the AR4 through its serial interface
Optional: ROS Integration
If you integrate the AR4 into ROS (Robot Operating System), the process becomes much more modular and scalable:
You can define the AR4 as a ROS robot with a full URDF and joint interfaces
Use MoveIt for kinematics, path planning, and trajectory generation
Use a simple ROS node to convert FollowJointTrajectory messages into the AR4’s serial command format
GR00T (or any other AI controller) can then publish goals or trajectories directly into ROS topics, and the rest is handled automatically
This approach aligns with how NVIDIA’s GR00T is expected to interface with robots: via ROS-based systems where high-level goals are translated into motion via standard interfaces.
Wanted to provide a link to the following tutorial series as a primer for the different models that you can use to train a robot arm. Huggy face working with LeRobot came out with a 3D printable robot arm with precision digital servos that output position. They recently held a robot arm hackathon using the Lerobot designed SO-101 arm.
The tutorial will take you through training on the ACT model(no prior knowledge), GR00T and Pi0 which have both been generally trained. What also seems to be a common theme is research lab X trains a new model on robot arm Y and then sends it to research lab A and they test without modification on robot arm W.
The outputs of the various models have normalized positions -1 to 1 and will have your standard 6 motor arm positions and work with in theory two cameras(one for the gripper) and one for the overview of the field of view. In the training examples of SO-101 arm all three models can be trained where the magic is the black box of the actual model that you don't need to worry about. In the case of the ACT model it is a train from scratch to do one specific task. The cameras are the primary input and it takes the position of each joint for multiple 50+ training sessions and calculates the weights to perform that task given that you will have variation of starting location and the destination with the classic example of pick the ball up from the bowl and put in the box. Move the bowl or box and the ACT model will probably not work. If the bowl contains a bunch of balls then it may be able to figure out how to adjust for picking up a new ball. Even the training needs to be consistent in that if you take two different paths for picking up the ball and dropping the ball it will impact the overall training viewing the one off as noise.
The GROOT and Pi0 model have been generally trained on lots and lots of demos so it understands basic concepts and thus when you train it for your task it can use prior training to adapt to solve the problem where it hasn't seen the solution prior. They are also much bigger models but the key is it uses only video as the input and a text command to tell it the task to complete. The outputs are per motor in the range of -1 to 1 and will output "PID like" values for the next step. This is the solution for the models need time to process the input so let it decide the next 10 micro steps per decision and do that as fast as possible. To get an AR4 running with these models given the mechanical differences from a classic 6 degree of freedome robot may not be that difficult. You would need a training arm that has the basic feel of the main arm where you get the servo positions that are then sent to the actual robot arm for position control. The human doing the training watches the video stream and completes the tasks. Those motor values with each camera image are recorded. You then feed those values back to the model for retraining and it learns how to move the arm but in reference to each successive image.
Based on me trying to keep up with this rapidly changing feel I think ROS is done and that can be extended to programming a robot is done. You will have the need to control high level commands but the models will take care of the task completion and if it doesn't know how to do it then just needs to be shown how to do it.
This is a good presentation from the group that developed pi0 model where they started out with the goal of being able to fold laundry. Lots of different models and what they figured out trying to train a robot arm on a specific task was hard to find improvements. Use a robot arm model that has been trained on many tasks then training on a new task you get significant improvements on that task. This theme has been presented by google and others at a couple conferences I have attended prior.
For the AR4 arm adapting a 3D printed version that for each joint has enough motor like resistance and high quality position measurement should allow that arm to be used for training/moving an actual AR4 arm and plug into the various models that are being developed/tested on the Huggy Face/LeRobot S-101 arm. This field is going to explode given the problem of programming a robot arm has been abstracted out to a training problem and no code needs to be written. If any interest in getting the AR4 arm as a replacement for the S-101 it is on our allocate some time todo list. The S-101 arm is actually not bad and the servos with 3D printed parts $199 and fully assembled $299. Thus will become the standard for robot arm model development/testing.
