A key challenge in intelligent robotics is creating robots that are capable of directly
interacting with the world around them to achieve their goals. The last decade has seen …

A generally intelligent agent faces a dilemma: it requires a complex sensorimotor space to
be capable of solving a wide range of problems, but many tasks are only feasible given the …

Robot learning is critically enabled by the availability of appropriate state representations.
We propose a robotics-specific approach to learning such state representations. As robots …

With the complication of the subjects and environment of the machine learning, feature
selection methods have been used more frequently as an effective mean of dimension …

State abstractions are often used to reduce the complexity of model-based reinforcement
learning when only limited quantities of data are available. However, choosing the …

A long-term goal of reinforcement learning agents is to be able to perform tasks in complex
real-world scenarios. The use of external information is one way of scaling agents to more …

Reinforcement learning from demonstration (RLfD) is considered to be a promising
approach to improve reinforcement learning (RL) by leveraging expert demonstrations as …

Modular reinforcement learning (MRL) decomposes a monolithic multiple-goal problem into
modules that solve a portion of the original problem. The modules' action preferences are …

In order to solve the problem of stability control for biped robots, the concept of stability
training is proposed by using a training platform to exert random disturbance with amplitude …

Reinforcement learning (RL) problems are hard to solve in a robotics context as classical
algorithms rely on discrete representations of actions and states, but in robotics both are …