Reinforcement Learning (RL) has achieved tremendous success in many complex decision-
making tasks. However, safety concerns are raised during deploying RL in real-world …

In the context of robotics and automation, learning from demonstration (LfD) is the paradigm
in which robots acquire new skills by learning to imitate an expert. The choice of LfD over …

We investigated whether deep reinforcement learning (deep RL) is able to synthesize
sophisticated and safe movement skills for a low-cost, miniature humanoid robot that can be …

The virtual-to-real paradigm, ie, training models on virtual data and then applying them to
solve real-world problems, has attracted more and more attention from various domains by …

The growing demand for robots able to act autonomously in complex scenarios has widely
accelerated the introduction of Reinforcement Learning (RL) in robots control applications …

We consider the problem of transferring policies to the real world by training on a distribution
of simulated scenarios. Rather than manually tuning the randomization of simulations, we …

Learning to combine control at the level of joint torques with longer-term goal-directed
behavior is a long-standing challenge for physically embodied artificial agents. Intelligent …

Sim-to-real transfer is a powerful paradigm for robotic reinforcement learning. The ability to
train policies in simulation enables safe exploration and large-scale data collection quickly …

Robot learning in simulation is a promising alternative to the prohibitive sample cost of
learning in the physical world. Unfortunately, policies learned in simulation often perform …

As learning-based approaches progress towards automating robot controllers design,
transferring learned policies to new domains with different dynamics (eg sim-to-real transfer) …