SofToss: Learning to Throw Objects With a Soft Robot

doi:10.1109/MRA.2023.3310865

SofToss: Learning to Throw Objects With a Soft Robot

2024-12-01

11

OA

AI

D

Diego Bianchi *

M

Michele Gabrio Antonelli

DOI：10.1109/MRA.2023.3310865

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In this paper, we present, for the first time, a soft robot control system (SofToss) capable of throwing life-size objects toward target positions. SofToss is an open-loop controller based on deep reinforcement learning that generates, given the target position, an actuation pattern for the tossing task. To deal with the high non-linearity of the dynamics of soft robots, we deployed a neural network to learn the relationship between the actuation pattern and the target landing position, i.e., the direct model of the task. Then, a reinforcement learning method is used to predict the actuation pattern given the goal position. The proposed controller was tested on a modular soft robotic arm, I-Support, by tossing four objects of different shapes and weights in 140-mm squared target boxes. We registered a success rate of almost 65% of the throws in two actuation modalities (i.e., partial, keeping one module of the soft arm passive, and complete, with both modules active). This performance raises to 85% if one can choose the number of modules to actuate for each throwing direction. Furthermore, the results show that the proposed learning-based real-time controller achieves performance comparable to an optimization-based non-real-time controller. Our study contributes to the foundations for bringing soft robots into everyday life and industry, by performing more complex, dynamic tasks.

Keywords:

Robots

Soft robotics

Task analysis

Manipulators

Trajectory

Grippers

Actuators

SofToss: Learning to Throw Objects With a Soft Robot

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