Creating autonomous robots that can actively explore the environment, acquire knowledge
and learn skills continuously is the ultimate achievement envisioned in cognitive and …

Decision-making is challenging in robotics environments with continuous object-centric
states, continuous actions, long horizons, and sparse feedback. Hierarchical approaches …

Nowadays, radio broadcasting plays an important role in people's daily life. However,
unauthorized broadcasting stations may seriously interfere with normal broadcastings and …

3D spatial perception is the problem of building and maintaining an actionable and
persistent representation of the environment in real-time using sensor data and prior …

Planning in realistic environments requires searching in large planning spaces. Affordances
are a powerful concept to simplify this search, because they model what actions can be …

Despite of achieving great success in real life, Deep Reinforcement Learning (DRL) is still
suffering from three critical issues, which are data efficiency, lack of the interpretability and …

We address the problem of efficiently learning high-level abstractions for task-level robot
planning. Existing approaches require large amounts of data and fail to generalize learned …

Symbolic planning and reasoning are powerful tools for robots tackling complex tasks.
However, the need to manually design the symbols restrict their applicability, especially for …

Problems which require both long-horizon planning and continuous control capabilities
pose significant challenges to existing reinforcement learning agents. In this paper we …

Enabling robots to learn tasks and follow instructions as easily as humans is important for
many real-world robot applications. Previous approaches have applied machine learning to …