Henry Zhu

TL;DR: Soft Actor-Critic (SAC), the recently introduced off-policy actor-critic algorithm based on the maximum entropy RL framework, achieves state-of-the-art performance, outperforming prior on-policy and off- policy methods in sample-efficiency and asymptotic performance.

TL;DR: It is shown that contact-rich manipulation behavior with multi-fingered hands can be learned by directly training with model-free deep RL algorithms in the real world, with minimal additional assumption and without the aid of simulation, indicating that direct deep RL training in thereal world is a viable and practical alternative to simulation and model-based control.

TL;DR: This work discusses the required elements of a robotic system that can continually and autonomously improve with data collected in the real world, and proposes a particular instantiation of such a system, and demonstrates the efficacy of this proposed system on dexterous robotic manipulation tasks in simulation and thereal world.

TL;DR: This work proposes an extensible set of continuous control benchmark tasks for each robot, and provides benchmark scores on an initial set of tasks using a variety of learning-based methods, and shows that they can be replicated across copies of the robots located in different institutions.

TL;DR: In this paper, the authors discuss the elements that are needed for a robotic learning system that can continually and autonomously improve with data collected in the real world, using dexterous manipulation as their case study.