In my final OpenThink post, I’d like to explore the differences between classical robotics and end-to-end deep learning approaches. In one of my previous posts, I discussed some of the high-level challenges of integrating AI into robotic systems. Now, let’s dive into the strengths and weaknesses of the classical robotic frameworks compared to the more recently popularized learning-centric approaches.
Classical Robotics
The classical approach, which has been used in robotics for decades, relies on separating a robot’s behavior into specific tasks such as perception, path planning, and control. Each task is managed by a distinct module. For example, in manufacturing, robotic arms use this approach for precise, repetitive tasks. In this framework, perception helps the robot understand its surroundings, path planning computes the routes, and control ensures the robot follows the routes accurately.
Pros:
– Modularity allows experts to develop and optimize each module independently and use different strategies interchangeably.
– Predictable behavior due to well-defined rules, making robots easier to debug.
– Reliability is backed by theoretical mathematical proofs to guarantee that solutions are available, and that the robot will not fail or damage itself.
Cons:
– Integrating various modules can be complex, especially in dynamic environments.
– Development is time-intensive, requiring significant expertise.
– Simplifications or presumptions in theoretical models may not always reflect real-world scenarios.
End-to-End RL in Robotics
End-to-end reinforcement learning (RL) in robotics, a subset of machine learning, adopts a different approach. Here, a robot learns from experiences, often using simulations to gather data. It directly maps sensory inputs to actions. Autonomous vehicles, for example, use RL to learn complex navigation tasks.
Pros:
– Flexibility and adaptability in new or complex environments.
– Simplified design without the need for intricate module integration.
– Ability to exhibit emergent behaviors, like a robot dog from ETH Zurich learning an energy-efficient sideways gait.
Cons:
– High computational resource and data requirements.
– Unpredictability in behavior, raising concerns in safety-critical applications.
– Challenges in defining precise, comprehensive reward functions for complex tasks.
Conclusion
Both classical and RL-based frameworks hold significant value in robotics. The choice often depends on the application: classical methods are well-suited for structured environments and tasks requiring high reliability and predictability. In contrast, RL excels in adaptive behavior and learning in complex, unstructured environments. The future of robotics may lie in a hybrid approach, combining the reliability of classical methods with the adaptability of RL. For instance, Disney has recently started testing small droid robots in their parks, which reflects this balance. They specify that they use RL to learn the expressive motions provided by their animators, while likely relying on classical control to enable the droids to navigate in a group.
I am excited to be a part of this field of research and I think the next decade will hold incredible advancement in robotics! Thanks for reading!
Image generated with Dall-E 3
