A hilarious new video reveals the clumsy progress of AI 'parkour,' as scientists work to teach computer systems how to navigate ‘challenging terrains and obstacles.’
DeepMind researchers have trained a number of simulated bodies, including a headless ‘walker,’ a four-legged ‘ant,’ and a 3D humanoid, to learn more complex behaviours as they carry out different locomotion tasks.
The results, while comical, show how these systems can learn to improve their own techniques as they interact with the different environments, eventually allowing them to run, jump, crouch and turn as needed.
In a new paper published to arXiv, researchers from Google's DeepMind explain how simple reward functions can lead to ‘rich and robust behaviours,’ given a complex environment to learn in.
The researchers set their simulations up against several obstacles, from hurdles to wall slalom, to help the AI characters teach themselves the best ways to get from one point to another.
And, footage from the study offers a hilarious look into the trial-and-error process.
As the characters run around throughout each simulated environment, they almost seem intoxicated as they flail, fall, collide with walls, and appear to trip over their own feet.
But, over time, they become far more successful in their efforts to navigate the different types of terrain.
As the team explains in the paper, the environments presented to the simulated bodies are of varying levels of difficulty, providing a setting in which they must come up with locomotion skills of increasing complexity to overcome the challenges.
‘In this work, we explore how sophisticated behaviours can emerge from scratch from the body interacting with the environment using only simple high-level objectives, such as moving forward without falling,’ researchers explain in a new post on the DeepMind blog.
‘Specifically, we trained agents with a variety of simulated bodies to make progress across diverse terrains, which require jumping, turning, and crouching.
‘The results show our agents develop these complex skills without receiving specific instructions, an approach that can be applied to train our systems for multiple, distinct simulated bodies.’
The approach relies on a reinforcement learning algorithm, developed using components from several recent deep learning systems.
According to the researchers, this type of method could help AI systems to achieve flexible and natural behaviours which can grow as they’re exposed to different situations.
Sophisticated motor control is a ‘hallmark of physical intelligence,’ the researchers write in the blog, allowing for everything from a monkey’s controlled movements through the trees to the complex navigation of a football player on the field.
And, as artificial intelligence continues to improve, they explain, these capabilities could soon allow computers to take on more complicated tasks.