The study of zero-shot generalisation (ZSG) in deep Reinforcement Learning (RL) aims to
produce RL algorithms whose policies generalise well to novel unseen situations at …

How do humans navigate to target objects in novel scenes? Do we use the
semantic/functional priors we have built over years to efficiently search and navigate? For …

Reinforcement Learning (RL) of contact-rich manipulation tasks has yielded impressive
results in recent years. While many studies in RL focus on varying the observation space or …

Policy search methods in reinforcement learning have demonstrated success in scaling up
to larger problems beyond toy examples. However, deploying these methods on real robots …

Understanding the gap between simulation and reality is critical for reinforcement learning
with legged robots, which are largely trained in simulation. However, recent work has …

We consider reinforcement learning in input-driven environments, where an exogenous,
stochastic input process affects the dynamics of the system. Input processes arise in many …

Reinforcement learning (RL) is promising for complicated stochastic nonlinear control
problems. Without using a mathematical model, an optimal controller can be learned from …

Combining domain randomization and reinforcement learning is a widely used approach to
obtain control policies that can bridge the gap between simulation and reality. However …

Model-free Reinforcement Learning (RL) offers an attractive approach to learn control
policies for high dimensional systems, but its relatively poor sample complexity often …

This survey provides an overview of combining federated learning (FL) and control to
enhance adaptability, scalability, generalization, and privacy in (nonlinear) control …