Robots have been useful in environments that can be carefully controlled, such
as those commonly found in industrial settings (e.g. assembly lines). However,
in unstructured settings like the home, we need robotic systems that are
adaptive to the diversity of the real world.
The interpretability of neural networks is becoming increasingly necessary, as
deep learning is being adopted in settings where accurate and justifiable
predictions are required. These applications range from finance to medical
imaging. However, deep neural networks are notorious for a lack of
justification. Explainable AI (XAI) attempts to bridge this divide between
accuracy and interpretability, but as we explain below, XAI justifies
decisions without interpreting the model directly.
Quadruped robot learning locomotion skills by imitating a dog.
Whether it’s a dog chasing after a ball, or a monkey swinging through the
trees, animals can effortlessly perform an incredibly rich repertoire of agile
locomotion skills. But designing controllers that enable legged robots to
replicate these agile behaviors can be a very challenging task. The superior
agility seen in animals, as compared to robots, might lead one to wonder: can
we create more agile robotic controllers with less effort by directly imitating
animals?
In this work, we present a framework for learning robotic locomotion skills by
imitating animals. Given a reference motion clip recorded from an animal (e.g.
a dog), our framework uses reinforcement learning to train a control policy
that enables a robot to imitate the motion in the real world. Then, by simply
providing the system with different reference motions, we are able to train a
quadruped robot to perform a diverse set of agile behaviors, ranging from fast
walking gaits to dynamic hops and turns. The policies are trained primarily in
simulation, and then transferred to the real world using a latent space
adaptation technique, which is able to efficiently adapt a policy using only a
few minutes of data from the real robot.
Deep reinforcement learning (RL) has achieved superhuman performance in
problems ranging from data center cooling to video games. RL policies
may soon be widely deployed, with research underway in autonomous driving,
negotiation and automated trading. Many potential applications are
safety-critical: automated trading failures caused Knight Capital to lose
USD 460M, while faulty autonomous vehicles have resulted in loss of
life.
Consequently, it is critical that RL policies are robust: both to naturally
occurring distribution shift, and to malicious attacks by adversaries.
Unfortunately, we find that RL policies which perform at a high-level in normal
situations can harbor serious vulnerabilities which can be exploited by an
adversary.
Reinforcement learning has seen a great deal of success in solving complex decision making problems ranging from robotics to games to supply chain management to recommender systems. Despite their success, deep reinforcement learning algorithms can be exceptionally difficult to use, due to unstable training, sensitivity to hyperparameters, and generally unpredictable and poorly understood convergence properties. Multiple explanations, and corresponding solutions, have been proposed for improving the stability of such methods, and we have seen good progress over the last few years on these algorithms. In this blog post, we will dive deep into analyzing a central and underexplored reason behind some of the problems with the class of deep RL algorithms based on dynamic programming, which encompass the popular DQN and soft actor-critic (SAC) algorithms – the detrimental connection between data distributions and learned models.
