Data efficient Robotic Object Throwing with Model-Based Reinforcement Learning

TL;DR

This paper presents MC-PILOT, a model-based reinforcement learning framework that enables efficient and accurate robotic pick-and-throw tasks, outperforming existing methods in generalization and real-world application.

Contribution

The paper introduces MC-PILOT, a novel model-based reinforcement learning approach for robotic pick-and-throw, combining data-driven modeling with policy optimization for improved efficiency and generalization.

Findings

MC-PILOT outperforms analytical and model-free methods in simulations.

The approach generalizes rapidly to new targets.

Real-world tests confirm superior performance with a Franka Emika Panda robot.

Abstract

Pick-and-place (PnP) operations, featuring object grasping and trajectory planning, are fundamental in industrial robotics applications. Despite many advancements in the field, PnP is limited by workspace constraints, reducing flexibility. Pick-and-throw (PnT) is a promising alternative where the robot throws objects to target locations, leveraging extrinsic resources like gravity to improve efficiency and expand the workspace. However, PnT execution is complex, requiring precise coordination of high-speed movements and object dynamics. Solutions to the PnT problem are categorized into analytical and learning-based approaches. Analytical methods focus on system modeling and trajectory generation but are time-consuming and offer limited generalization. Learning-based solutions, in particular Model-Free Reinforcement Learning (MFRL), offer automation and adaptability but require extensive interaction time. This paper introduces a Model-Based Reinforcement Learning (MBRL) framework, MC-PILOT, which combines data-driven modeling with policy optimization for efficient and accurate PnT tasks. MC-PILOT accounts for model uncertainties and release errors, demonstrating superior performance in simulations and real-world tests with a Franka Emika Panda manipulator. The proposed approach generalizes rapidly to new targets, offering advantages over analytical and Model-Free methods.