Abstract: Soft robotic systems are known for their flexibility and adaptability, but traditional physics-based models struggle to capture their complex, nonlinear behaviors. This study explores a data-driven approach to modeling the volume–flow–pressure relationship in hydraulic soft actuators, focusing on low-complexity models with high accuracy. We perform regression … [Read more...] about Simplifying Data-Driven Modeling of the Volume–Flow–Pressure Relationship in Hydraulic Soft Robotic Actuators: A Practical and Balanced Solution
Dynamics
Simplifying Data-Driven Modeling of the Volume–Flow–Pressure Relationship in Hydraulic Soft Robotic Actuators: A Practical and Balanced Solution
Abstract: Soft robotic systems are known for their flexibility and adaptability, but traditional physics-based models struggle to capture their complex, nonlinear behaviors. This study explores a data-driven approach to modeling the volume–flow–pressure relationship in hydraulic soft actuators, focusing on low-complexity models with high accuracy. We perform regression … [Read more...] about Simplifying Data-Driven Modeling of the Volume–Flow–Pressure Relationship in Hydraulic Soft Robotic Actuators: A Practical and Balanced Solution
Curriculum-Based Reinforcement Learning for Quadrupedal Jumping: A Reference-Free Design
Deep reinforcement learning (DRL) has emerged as a promising solution to mastering explosive and versatile quadrupedal jumping skills. However, current DRL-based frameworks usually rely on pre-existing reference trajectories obtained by capturing animal motions or transferring experience from existing controllers. This work aims to prove that learning dynamic jumping is … [Read more...] about Curriculum-Based Reinforcement Learning for Quadrupedal Jumping: A Reference-Free Design
Human–Humanoid Robots’ Cross-Embodiment Behavior-Skill Transfer Using Decomposed Adversarial Learning From Demonstration: HOTU, a Human–Humanoid Robots’ Skill Transfer Framework
Humanoid robots are envisioned as embodied intelligent agents capable of performing a wide range of human-level loco-manipulation tasks, particularly in scenarios that require strenuous and repetitive labor. However, learning these skills is challenging due to the high degrees of freedom of humanoid robots, and collecting sufficient training data for humanoid is a laborious … [Read more...] about Human–Humanoid Robots’ Cross-Embodiment Behavior-Skill Transfer Using Decomposed Adversarial Learning From Demonstration: HOTU, a Human–Humanoid Robots’ Skill Transfer Framework
I-CTRL: Imitation to Control Humanoid Robots Through Bounded Residual Reinforcement Learning
Humanoid robots have the potential to mimic human motions with high visual fidelity, yet translating these motions into practical physical execution remains a significant challenge. Existing techniques in the graphics community often prioritize visual fidelity over physics-based feasibility, posing a significant challenge for deploying bipedal systems in practical applications. … [Read more...] about I-CTRL: Imitation to Control Humanoid Robots Through Bounded Residual Reinforcement Learning
