fluid-structure interaction

This work addresses the challenges of tight dynamical coupling and numerical instability in strongly coupled multiphysics simulation of fluid–robot interactions. We propose a unified optimization framework grounded in the principle of least action and discrete variational mechanics, enabling the first implicit, numerically stable, and fully coupled modeling of rigid-body robot dynamics with the incompressible Navier–Stokes equations. A key innovation is the construction of a physically consistent and numerically well-conditioned generalized no-slip constraint, supporting multi-body systems. By integrating an improved immersed boundary method with implicit time integration, we develop a coordinated multiphysics solver. The framework achieves high physical fidelity on benchmark problems—including Poiseuille flow and cylinder wake flow—and successfully enables the design and real-world validation of novel swimming robot locomotion strategies, significantly improving sim-to-real transfer fidelity.

In soft robot design, shape, material distribution, and actuation are highly coupled, rendering traditional approaches inefficient for joint optimization due to the high computational cost of high-dimensional nonlinear simulations and the inapplicability of gradient-based methods. This work proposes a low-dimensional, structured design embedding based on shared basis functions that unifies these three aspects through a continuous deformation mapping and spatial material field encoding within a common latent space. The representational capacity of this approach predictably improves with the number of basis functions, remains compatible with black-box simulators, and enables end-to-end joint optimization. Experiments across multiple dynamic tasks demonstrate that the method achieves significantly better performance than neural network and voxel-based baselines using fewer parameters, and consistently outperforms sequential optimization strategies.

Real-time, closed-loop strain regulation across the entire body of continuum soft robots—characterized by multi-physics coupling and multi-scale dynamics—remains challenging due to high dimensionality, strong nonlinearities, and coupled material–geometric effects. Method: This paper proposes a model-driven control framework integrating singular perturbation theory with nonlinear backstepping. By performing multi-scale dynamic modeling and explicit time-scale separation, subsystems are decoupled to enable reduced-order yet high-fidelity controller design; material and geometric nonlinearities are explicitly embedded to enhance embodied compliance. Contribution/Results: To our knowledge, this is the first application of singular perturbation analysis to strain regulation in infinite-dimensional soft robots. The approach significantly improves closed-loop stability and convergence speed. In experimental validation on an octopus-inspired single-arm robot, whole-body strain converges to the desired equilibrium within milliseconds, reducing regulation time by 42% while substantially improving control accuracy and robustness against disturbances and modeling uncertainties.

This work addresses the challenge of precise end-effector localization of slender, deformable objects—such as cables—in high-speed dynamic scenarios, transcending conventional quasi-static and massless assumptions. It pioneers the integration of soft robotics dynamic modeling principles into linear object manipulation. We propose a fully model-driven control framework based on functional strain parameterization, enabling analytically verifiable Lyapunov-based closed-loop stability and steady-state convergence of shape regulation. The method synergistically combines nonlinear feedback shaping with real-time 7-DoF robotic arm closed-loop control, achieving high-accuracy in-plane end-position-and-orientation regulation across six distinct cable types. Experiments demonstrate substantial improvements in both dynamic responsiveness and steady-state accuracy for deformable object manipulation under non-quasi-static conditions. To our knowledge, this is the first theoretically provable and engineering-deployable model-driven solution for complex compliant object manipulation in embodied intelligence systems.

Modeling and simulating multi-material interactions—soft bodies, articulated rigid bodies, and cloth—in robotic manipulation remains challenging due to non-differentiable coupled dynamics, severe rendering artifacts, and difficulties in unifying heterogeneous material representations. Method: We propose the first unified differentiable physics simulation framework, integrating the Material Point Method (MPM) for continuum modeling, a prediction-based MPM contact model, and a local-penetration-aware Signed Distance Field (SDF) reconstruction algorithm—enabling explicit, artifact-free, bidirectional coupling among soft, rigid, and cloth modalities. A fully end-to-end differentiable gradient propagation mechanism is further established. Contribution/Results: This work achieves the first fully explicit, differentiable, multi-modal coupling, enabling gradient-based control optimization. Extensive evaluation on representative tasks—including grasping, folding, and assembly—demonstrates high physical fidelity and optimization efficiency, significantly improving control performance for soft actuators and underactuated systems.

Soft robots exhibit large deformations, near-incompressibility, and complex contact interactions across multiphysics domains—posing significant modeling challenges that lead to simulation inaccuracies, numerical instability, and trade-offs between fidelity and scalability. To address these, we propose SORS: a high-fidelity, modular simulation platform for soft robotics. SORS introduces the first energy-driven, modular finite element framework, enabling user-defined constitutive laws and actuation models. It further incorporates a constraint-aware, sequential quadratic programming (SQP)-based nonlinear contact algorithm to ensure numerical stability and physical consistency. Validated via multimodal experimental calibration—including cantilever bending, pneumatic actuator deformation, and PokeFlex indentation—the platform achieves sub-millimeter deformation prediction accuracy. SORS successfully enables optimization of a soft-legged robot controller. To our knowledge, it is the first open-source soft robotics simulator that simultaneously delivers scalability, high fidelity, and usability.

Soft-bodied robot simulation faces dual challenges in modeling large deformations and complex contacts, with existing tools struggling to balance physical fidelity, computational efficiency, and control integration. This paper proposes a full-stack simulation and control framework grounded in discrete differential geometry. It features: (i) a fully vectorized NumPy implementation for high-performance computation; (ii) a penalty-energy-based implicit contact model unifying rigid–soft and soft–soft interactions; (iii) natural-strain-based PI feedback control; and (iv) modular coupling of energy models, actuation mechanisms, and machine learning components. Compared to the state-of-the-art Elastica library, our framework achieves a tenfold speedup while preserving comparable accuracy. It supports real-time trajectory tracking for rods, shells, and hybrid structures, and demonstrates robust sim-to-real transfer in experimental validation.

Existing computational tools struggle to simultaneously achieve high-fidelity dynamics of slender elastic rods, scalability to large ensembles, and flexibility in multiphysics coupling. This work presents the first open-source simulation framework based on Cosserat rod theory that integrates high fidelity, massive scalability, and multiphysics versatility. By leveraging high-performance core algorithms, shared-memory parallelization, efficient discretization schemes, and interoperable interfaces to external solvers, the framework attains teraflop-scale computational throughput. Its robustness and broad applicability are demonstrated across diverse scenarios—including nematic metamaterials, active matter collectives, ciliary arrays, soft magnetic microrobots, and schooling fish—thereby filling a critical gap in high-throughput studies of emergent behaviors in complex filamentous elastic systems.

This work addresses the vulnerability of humanoid robots to damage from falls in human environments and the safety risks posed by their rigid structures. The authors propose a novel co-design framework that integrates non-Newtonian fluid-based responsive soft materials, physics-simulation-driven protective structure optimization, and a learning-based active fall control strategy. The soft material remains compliant under normal conditions but instantaneously stiffens upon impact to dissipate energy effectively. Through joint optimization of these components, the system achieves high robustness and environmental safety. Validated on a full-scale, 42-kg humanoid robot, the approach significantly reduces peak impact forces and enables repeated high-energy falls—including 3-meter drops and stair tumbles—without hardware damage.

This study addresses the challenges in predicting deformation and achieving co-optimized design of hard-magnetic soft materials under magnetic actuation by proposing a unified effective shear modulus framework. This framework integrates classical inclusion theory, the Hill self-consistent model, and constrained kinematic relations, and employs an experimentally calibrated Mooney strain energy function to formulate a multiphysics constitutive model. Building upon this foundation, a material–structure concurrent topology optimization method is developed to simultaneously tailor structural density, magnetic particle distribution, and remanent magnetization orientation. The proposed framework successfully generates non-intuitive designs capable of achieving prescribed deformations—such as rotation, translation, and recovery—demonstrating its versatility and precise controllability across single- and multi-loading scenarios and diverse design objectives.