Pneumatic artificial muscles (PAMs) enable compliant actuation for soft wearable, assistive, and interactive robots. When arranged antagonistically, PAMs can provide variable impedance through co-contraction but exhibit coupled, nonlinear, and hysteretic dynamics that challenge modeling and control. This paper presents a hybrid neural ordinary differential equation (Neural ODE) framework that embeds physical structure into a learned model of antagonistic PAM dynamics. The formulation combines parametric joint mechanics and pneumatic state dynamics with a neural network force component that captures antagonistic coupling and rate-dependent hysteresis. \rev{The forward model was trained on 29 selected co-contraction conditions and predicted joint motion and chamber pressures over 196 held-out conditions with a mean R$^2$ of 0.88.} An inverse formulation, derived from the learned dynamics, computes pressure commands offline for desired motion and stiffness profiles, tracked in closed loop during execution. Experimental validation demonstrates reliable stiffness control across 126-176 N/mm and consistent impedance behavior across operating velocities, in contrast to a static model, which shows degraded stiffness consistency at higher velocities.
Multi-Mode Pneumatic Artificial Muscles Driven by Hybrid Positive-Negative Pressure
Department of Mechanical Engineering（机械工程系）；Tsinghua University（清华大学）；Beijing Key Laboratory of Transformative High-end Manufacturing Equipment and Technology（北京 transformative 高端制造装备与技术重点实验室）
Fabric Pneumatic Artificial Muscle-Based Head-Neck Exosuit: Design, Modeling, and Evaluation
Department of Aerospace and Mechanical Engineering, University of Notre Dame（Notre Dame 大学航空航天与机械工程系）；Faculty of Information Technology and Bionics, Pázmány Péter Catholic University（帕兹曼·佩特天主教大学信息科技与生物医学系）；Department of Mechanical Engineering and Robotics Center, University of Utah（犹他大学机械工程系与机器人中心）
Muscle Coactivation in the Sky: Geometry and Pareto Optimality of Energy vs. Aerodynamic Promptness and Multirotors as Variable Stiffness Actuators
Robotics and Mechatronics Department, Electrical Engineering, Mathematics, and Computer Science (EEMCS) Faculty（代尔夫特理工大学机器人与机电系）；Department of Computer, Control and Management Engineering（罗马萨皮恩扎大学计算机、控制与管理工程系）
How do trout regulate patterns of muscle contraction to optimize propulsive efficiency during steady swimming
The College of River and Ocean Engineering, Chongqing Jiaotong University,Chongqing, 400016, China（重庆交通大学河流海洋工程学院）；State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu, 610065, China（四川大学水利与山区河流工程重点实验室）；Chongqing Deepflow Intelligent Computing Technology Co., Ltd, Chongqing, 401122, China（重庆深流智能计算技术有限公司）
Learning to Play Table Tennis From Scratch using Muscular Robots
MPI for Intelligent Systems（马克斯·普朗克智能系统研究所）；Technische Universität Darmstadt（达姆施塔特工业大学）；Facebook AI Research（脸书人工智能研究院）
像乒乓球这样的动态任务对人类来说相对容易学习，但对机器人来说却构成了重大挑战。这样的任务需要在存在飞行球和机器人的不精确状态估计的情况下对快速运动和精确定时进行精确控制。强化学习（RL）在从数据中学习复杂控制任务方面显示出了希望。然而，将基于步骤的强化学习应用于实际系统上的动态任务是安全关键的，因为强化学习需要在高速状态下安全地探索和失败数百万个时间步。在本文中，我们证明了使用无模型强化学习乒乓球的安全学习可以通过使用气动人工肌肉（PAM）驱动的机器人手臂来实现。PAM的柔软性和反向驱动特性防止系统离开其状态空间的安全区域。以这种方式，RL使机器人能够将平均具有5 m \s和12 m \s的真实球返回并粉碎到期望的着陆点。我们的设置允许代理学习这个安全关键任务（i）在算法中没有安全约束，（ii）同时直接在奖励函数中最大化返回球的速度（iii）使用直接作用于真实系统的低级控制的随机策略，以及（iv）在没有任何先验知识的情况下从头开始训练数千次试验（v）。此外，我们提出了HYSR，一个实用的混合模拟和真实的训练，避免在训练过程中播放真实的球随机重放记录的球轨迹模拟和应用行动的真实机器人。这项工作是第一个（a）故障安全学习的安全关键的动态任务，使用拟人机器人手臂，（b）学习精度要求的问题与PAM驱动的系统，尽管控制的挑战和（c）训练机器人打乒乓球没有真正的球。视频和数据集可在muscul…
