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
Dynamics
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
Enhancing Dexterity in Confined Spaces: Real-Time Motion Planning for Multifingered In-Hand Manipulation
Dexterous in-hand manipulation in robotics, particularly with multi-fingered robotic hands, poses significant challenges due to the intricate avoidance of collisions among fingers and the object being manipulated. Collision-free paths for all fingers must be generated in real-time, as the rapid changes in hand and finger positions necessitate instantaneous recalculations to … [Read more...] about Enhancing Dexterity in Confined Spaces: Real-Time Motion Planning for Multifingered In-Hand Manipulation
Terrain-Adaptive Locomotion Control for an Underwater Hexapod Robot: Sensing Leg–Terrain Interaction With Proprioceptive Sensors
An underwater hexapod robot, driven by six C-shaped legs and eight thrusters, has the potential to traverse diverse terrains with unknown deformable properties, which can lead to unknown leg–terrain interaction forces. However, it is hard to use exteroceptive sensors such as cameras and sonars to recognize these properties. Here we propose a method to perceive the interaction … [Read more...] about Terrain-Adaptive Locomotion Control for an Underwater Hexapod Robot: Sensing Leg–Terrain Interaction With Proprioceptive Sensors
