Reinforcement learning (RL) is a branch of machine learning that teaches agents how to learn from their own actions and rewards. RL has been applied to various domains, such as games, finance, and healthcare. But what about robotics? How can we use RL to train robots to perform complex tasks in dynamic and uncertain environments?
One of the main challenges of RL for robotics is the data efficiency problem. RL algorithms often require a large amount of data to learn a good policy, which can be costly and time-consuming to collect in the real world. Moreover, real-world data can be noisy, sparse, and inconsistent, which can degrade the performance and robustness of the learned policy. Therefore, RL for robotics needs to find ways to leverage both simulation and real-world data to achieve faster and better learning.
One of the biggest challenges to applying RL to robotics is that writing good reward functions is extremely difficult. In real life, reward functions have their own non-linear properties and often need to be learned as well.
Simulation is a powerful tool for RL, as it can provide a rich and diverse source of data without the physical constraints and risks of the real world. Simulation can also allow for faster and more scalable experimentation, as well as fine-grained control over the environment and the agent. Simulation can help RL algorithms to explore different scenarios, test different hypotheses, and learn from synthetic rewards. However, simulation also has its limitations, such as the reality gap, the computational cost, and the transferability issue.
Simulation is a critical foundation for building a base model to solve a problem. The initial phase of training a model to converge in solving a task requires significant brute-force across a wide search space and is best done in simulation. Once you have a base model to refine from, the search space is smaller because you're hopefully starting out much closer to the final solution. Gradient descent will get you to a converged policy faster if you already start out in the basin of attraction. Therefore it often makes sense to build your first model in simulation and then refine it with real-world samples.
Real-world data is essential for RL, as it can capture the complexity and unpredictability of the actual environment and the agent. Real-world data can also provide feedback and guidance for the RL algorithm, as well as validation and evaluation for the learned policy. Real-world data can help RL algorithms to adapt to changing conditions, generalize to new situations, and learn from natural rewards. However, real-world data also has its challenges, such as the safety, ethical, and legal implications, the data collection and annotation process, and the data quality and diversity issue.
Simulation is never a perfect match for real-world behavior, but if you can train in simulation and then refine the model with real-world data, you can close the sim-to-real gap in a more efficient way.
To leverage the advantages of both simulation and real-world data for RL, several methods have been proposed and developed. Domain randomization, for example, involves introducing random variations in the simulation parameters, such as the lighting, texture, shape, and physics of the objects and the agent. This can help to increase the diversity and robustness of the simulated data, and reduce the gap between simulation and reality. Domain adaptation adjusts the simulation or policy to match the real-world distribution. Sim-to-real transfer involves transferring the policy learned in simulation to the real world. Real-to-sim transfer transfers real-world data to the simulation. These methods can help to improve transferability and generalization of the policy from simulation to real world, reduce data efficiency problems, accelerate learning processes in the real world, and enrich simulation data by incorporating real-world feedback and guidance into learning processes.
How to get started on this approach: 1) Test your robot in a life-like controlled environment (maybe your lab), find 5 variables to collect data on - note the performance against some spectrum of reward 2) Next test your robot in the wild in a similar environment to your lab, but now note the difference between the 2 environments keeping the 5 variables constant- find similarities & differences & discover what risk factors posed the biggest threat in the wild 3) As you add more variables to the 2 test environments & collect more data from variations on your test runs you will create more robust robot operational functionality, given you reduce risk elements unknown in the wild This method should improve your sim-to-real transferability!
There are many examples of RL for robotics that use simulation and real-world data, such as OpenAI's Dactyl, which applies domain randomization and sim-to-real transfer to train a robotic hand to manipulate objects. Google's QT-Opt uses domain adaptation and real-to-sim transfer to train a robotic arm to grasp objects in the real world. Berkeley's BAIR Lab employs domain adaptation and sim-to-real transfer to teach a quadruped robot to walk on various terrains in simulation and then transfer the skill to the real world. All these projects demonstrate how RL can be used to bridge the gap between simulation and reality.
Research in Reinforcement Learning (RL) for robotics using simulation and real-world data is a promising and exciting area, yet it poses many open questions and challenges. In order to advance this field, future directions include designing more realistic and versatile simulations that capture the dynamics and diversity of the real world, developing efficient and effective methods for domain adaptation and transfer learning, balancing the trade-off between exploration and exploitation, safety and performance, and simplicity and complexity in RL for robotics, integrating more human knowledge and interaction into RL for robotics such as demonstrations, instructions, or feedback, and evaluating and benchmarking the performance and robustness of RL for robotics using simulation and real-world data.
Ultimately, I believe we need to learn both the policy and the reward function. Hand written reward functions often do not capture the subtleties of how robots need to perform in the real world. I'm a fan of using human preference (e.g. the robot did what the human expected) as an input to the reward function, or even as the reward function itself. By showing humans examples of candidate policies and asking humans to rate one vs. the other or pick one from a set of policies, it's possible to both learn the intrinsic reward function from human judgment and develop a policy that matches that reward function as part of the same training process.