Throwing Objects into A Moving Basket While Avoiding Obstacles
Hamidreza Kasaei, and Mohammadreza Kasaei

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…
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Taxonomy
TopicsRobot Manipulation and Learning · Reinforcement Learning in Robotics
