Data Collection Methods for Imitation Learning in Robotics

Educational Resources and Concepts
1

In recent years, data collection has become a cornerstone of training effective imitation learning models in robotics. Traditionally, this data was gathered in simulation environments, which offered a scalable and cost-effective approach. However, the reliance on simulation alone presents challenges, particularly when bridging the sim2real gap, where robots trained in simulated environments often underperform in real-world scenarios. With recent advancements in technology, there’s a growing belief that teleoperation and other methods using real, low-cost hardware may offer a more practical and efficient way to collect high-quality data for imitation learning. These new approaches are proving to be game-changers in training robots to handle complex real-world tasks.

The Sim2Real Challenge

One of the most significant hurdles in simulation-based data collection is the challenge of accurately modeling the robot and its environment. While simulations can replicate basic physics, they struggle with more complex interactions, particularly modeling contact between the robot and its surroundings. This includes tasks like grasping workpieces interacting with deformable surfaces, where the physics of contact are crucial. Simulations often underperform when replicating these interactions, leading to a mismatch between simulated and real-world performance.

Another key difficulty in simulations is replicating the visuals of realistic environments. Real-world scenes are filled with complex textures and lighting conditions that are difficult to simulate accurately. Simulated environments, even those built in advanced gaming engines like Unreal Engine or Unity, often oversimplify the rich details present in real-world visuals. This lack of realism can severely limit the robot’s ability to generalize from synthetic images to real-world tasks. Small discrepancies in lighting, object appearance, or camera perspectives can cause significant issues when transitioning from simulated training to real-world applications. For vision-robot systems, the challenge is not only in creating a visually rich simulation but also in ensuring that the robot’s learning can transfer effectively to the unpredictable variety of real-world scenes.

Teleoperation as a Data Collection Method

To overcome the limitations of simulation, researchers have turned to teleoperation as a powerful alternative for collecting data in real-world environments. Teleoperation involves human operators controlling the robot directly, which allows for real-time data collection in complex, dynamic environments that would be difficult to simulate.

A notable example of this shift is the introduction of the ACT and ALOHA frameworks, which acted as catalysts in the field of teleoperation-based data collection. By leveraging affordable, low-cost hardware, these frameworks made it possible to collect large amounts of data quickly and efficiently. Even smaller, cost-effective robots like the robotic arms from Tau Robotics have been used in teleoperation setups to gather valuable training data.

In addition to traditional teleoperation, innovative methods like using VR controllers and other tracking methods have gained popularity. These controllers provide an intuitive interface for controlling robots in a way that mimics human hand movements.

Open-source projects like the teleop project from SpesRobotics are making teleoperation more accessible by allowing operators to control a robot arm using just motion data from a smartphone, with no extra hardware or app installation needed.

New techniques are emerging that offer even more potential for imitation learning models. For instance, shadowing methods, like those seen in the HumanPlus project, allow robots to mimic human movement with increasing precision with nothing but a simple RGB camera. These methods could revolutionize the way robots learn complex tasks by directly observing human actions, rather than relying solely on simulated data or teleoperation.

What are your thoughts on the future of data collection in robotics? Are there other methods or tools you think will play a major role in shaping the next generation of imitation learning models? Feel free to share your experiences and insights!

2

Teleop or similar methods that digitize human behaviors are always the first step and are well studied. But I think there’s a two other things that help generating data.

1, traditional control schemes. For more structured applications such as racing in a track, traditional control schemes like pure pursuit and model predictive control can easily achieve the performance similar to that of the average human pilots. Similarly, for manipulators, many tasks can be done with planning stacks like moveit and vision stacks like Yolo. Depending on the nature of tasks, using model based methods to automate the data collection can be moy preferable than operating manually.

2, once the robot can more or less carry out the task, we can make our artificial intelligence centipede that makes life easier. We can simply run the current model, throw the successful runs back into the training set. To prevent degeneration, we need to add noise to the task to augment the data, and train multiple models instead of just one to facilitate diversity.

3

I also think training models against specific constraints could also be a huge area going forward in terms of being able to generate data that can bridge the Sim2Real gap. For example, training diffusion models against engineering constraints helps create data closer to the Real world while also being more ‘informative’.
Here’s an article that generates images of cars using diffusion models while enforcing constraints on drag coefficients.

The ideas underpinning this work, can be applied across fields to help in data generation.