Throwing Objects into A Moving Basket While Avoiding Obstacles

TL;DR

This paper presents a deep reinforcement learning method enabling robots to accurately throw objects into moving baskets while avoiding obstacles, enhancing reachability and execution speed in complex environments.

Contribution

First to address obstacle avoidance in robotic throwing using deep reinforcement learning, allowing precise throws into moving targets outside kinematic reach.

Findings

Robots successfully threw objects into moving baskets with obstacle avoidance.

The approach generalized well to new locations and objects.

Experiments confirmed effectiveness in simulation and real robots.

Abstract

The capabilities of a robot will be increased significantly by exploiting throwing behavior. In particular, throwing will enable robots to rapidly place the object into the target basket, located outside its feasible kinematic space, without traveling to the desired location. In previous approaches, the robot often learned a parameterized throwing kernel through analytical approaches, imitation learning, or hand-coding. There are many situations in which such approaches do not work/generalize well due to various object shapes, heterogeneous mass distribution, and also obstacles that might be presented in the environment. It is obvious that a method is needed to modulate the throwing kernel through its meta parameters. In this paper, we tackle object throwing problem through a deep reinforcement learning approach that enables robots to precisely throw objects into moving baskets while there are obstacles obstructing the path. To the best of our knowledge, we are the first group that addresses throwing objects with obstacle avoidance. Such a throwing skill not only increases the physical reachability of a robot arm but also improves the execution time. In particular, the robot detects the pose of the target object, basket, and obstacle at each time step, predicts the proper grasp configuration for the target object, and then infers appropriate parameters to throw the object into the basket. Due to safety constraints, we develop a simulation environment in Gazebo to train the robot and then use the learned policy in real-robot directly. To assess the performers of the proposed approach, we perform extensive sets of experiments in both simulation and real robots in three scenarios. Experimental results showed that the robot could precisely throw a target object into the basket outside its kinematic range and generalize well to new locations and objects without colliding with obstacles.