PourNet: Robust Robotic Pouring Through Curriculum and Curiosity-based Reinforcement Learning

Abstract

Pouring liquids accurately into containers is one of the most challenging tasks for robots as they are unaware of the complex fluid dynamics and the behavior of liquids when pouring. Therefore, it is not possible to formulate a generic pouring policy for real-time applications. In this paper, we propose PourNet, as a generalized solution to pouring different liquids into containers. PourNet is a hybrid planner that uses deep reinforcement learning, for end-effector planning, and Nonlinear Model Predictive Control, for joint planning. In this work, we introduce a novel simulation environment using Unity3D and NVIDIA-Flex to train our agents. By effective choice of the state space, action space and the reward functions, we allow for a direct sim-to-real transfer of the learned skills without additional training. In the simulation, PourNet outperforms state-of-the-art by an average of 4.9g deviation for water-like, and 9.2g deviation for honey-like liquids. In the real-world scenario using Kinova Movo Platform, PourNet achieves an average pouring deviation of 2.3g for dish soap when using a novel pouring container. The average pouring deviation measured for water was 5.5g. All comprehensive experiments and the simulation environment is available at: http://cxdcxd.github.io/RRS/.