Learning control policies from high-dimensional robot demonstrations remains challenging due to the need for large datasets, limited generalization, and high model complexity. These limitations hinder data-efficient learning and reactive deployment from limited demonstrations. This article introduces Laplacian Eigenmap-based modeling of nonlinear dynamical systems (LEMON-DS), a compact and data-efficient framework for one-shot learning of stable control policies from high-dimensional demonstrations. LEMON-DS proceeds in the following three stages: first, a graph Laplacian-based spectral embedding that reveals a quasi-linear latent structure from a single trajectory, second, defining a globally asymptotically stable dynamical system (DS) in the latent space, and third, learning a diffeomorphic mapping that reconstructs the demonstrated behavior. Collectively, this pipeline yields a lightweight reactive control policy in the form of a DS that is parameter-efficient, globally asymptotically stable, and faithfully reproduces the demonstrated behavior generalizing across initial conditions. We show empirical performance across diverse robot tasks up to 23 dimensions, outperforming prior approaches in accuracy and model compactness.
Artificial muscles embody human aspirations for engineering lifelike robotic movements. This paper introduces an architecture for Inflatable Fluid-Driven Origami-Inspired Artificial Muscles (IN-FOAMs). A typical IN-FOAM consists of an inflatable skeleton enclosed within an outer skin, which can be driven using a combination of positive and negative pressures (e.g., compressed air and vacuum). IN-FOAMs are manufactured using low-cost heat-sealable sheet materials through heat-pressing and heat-sealing processes. Thus, they can be ultra-thin when not actuated, making them flexible, lightweight, and portable. The skeleton patterns are programmable, enabling a variety of motions, including contracting, bending, twisting, and rotating, based on specific skeleton designs. We conducted comprehensive experimental, theoretical, and numerical studies to investigate IN-FOAM's basic mechanical behavior and properties. The results show that IN-FOAM's output force and contraction can be tuned through multiple operation modes with the applied hybrid positive-negative pressure. Additionally, we propose multilayer skeleton structures to enhance the contraction ratio further, and we demonstrate a multi-channel skeleton approach that allows the integration of multiple motion modes into a single IN-FOAM. These findings indicate that IN-FOAMs hold great potential for future applications in flexible wearable devices and compact soft robotic systems.
Engineered assembloids fabricated from tissue spheroids hold immense promise for developmental biology, disease modeling, and regenerative medicine. However, fabricating heterogeneous assembloids with precise spatial patterning remains a critical bottleneck, often reliant on manual pipetting that lacks scalability and reproducibility. While magnetic microrobotics, which transforms spheroids into controllable robots, offers a non-invasive alternative, it faces a fundamental challenge: the global input of magnetic actuation. A single command moves all robots simultaneously, leading to coupled motion and frequent assembly failures. Here, we present a collaborative control framework that overcomes this limitation by leveraging local constraints and a novel motion decoupling strategy. We reformulate the high-dimensional, coupled multi-robot planning problem into a low-dimensional aggregate space, effectively transforming the assembly task into a dynamic sequential decision problem. This framework, coupled with a graph-based dynamic path optimization algorithm, enables deterministic, collision-free assembly. Experimental validation demonstrates a 78.57% higher success rate and a 33.20% higher assembly efficiency compared to conventional strategies. This work establishes a foundational engineering principle for assembloid fabrication, transitioning the process from a qualitative experiments to a controllable and programmable engineering discipline, thereby unlocking the potential for deterministic construction of complex biological structures.
Work-related musculoskeletal disorders (WMDs) affect a high percentage of operators performing repeated weight lifting and load carrying in industrial scenarios. Since upper limb muscles are affected in the process, the assistance provided by upper body exoskeletons is increasingly needed to prevent WMDs and their consequent cost to the health system. This article presents the evaluation of Flexos, a portable, bilateral, shoulder exoskeleton prototype designed to assist logistic and industrial operators in performing occupational tasks. An in-lab assessment was conducted on twelve healthy subjects-9 males, 3 females-to evaluate Flexos capability in assisting the user during the execution of isometric, dynamic, and carrying-load tasks. Different metrics were extracted from time-series signals to assess the effort related to five targeted muscles surrounding the shoulder complex. Despite the limited experimental size and the prototypal level of the device, Flexos managed to cover almost all the shoulders range of motion-89.2% flexion/extension, and 88.4% internal/external rotation-and to globally decrease muscular activity in occupational activities, particularly when isometric contractions are required for a prolonged time, with average reductions of -27.2% for the static task, -18.6% for the dynamic task and -23.4% for the carrying-load task.
This article presents a formal specification framework for planning and control of autonomous robots, focusing on the challenge of managing complex tradeoffs among multiple potentially conflicting objectives. These include hierarchical relationships and noncomparable objectives, some of which may be too complex to be captured by standard additive cost functions. We leverage the rulebook formalism to represent such objectives and their relationships and formulate two control synthesis problems: single-strategy synthesis, which seeks one optimal strategy, and complete synthesis, which computes the full set of optimal strategies with respect to a rulebook, analogous to the Pareto front in multiobjective planning. We show that our formulation generalizes existing temporal logic-based and optimization-based planning and control, providing a unifying framework across robotics, formal methods, control theory, and operation research. For single-strategy synthesis, we identify tractable subclasses and present a polynomial-time algorithm that accommodates richer combinations of objectives than prior work. For complete synthesis, we introduce an algorithm to compute all optimal solutions and analyze its computational complexity. In both cases, we present case studies that include complex multiobjective planning problems and demonstrate the practical effectiveness of our approach compared to existing methods.
Designing an anthropomorphic robotic hand with few actuators while replicating the dexterous motion capabilities of the human hand remains a challenging problem. In this work, a kinematic synergy analysis method is proposed to identify joints with high motion independence and to quantify the synergistic motion characteristics of joints with strong motion dependence. Based on these findings, design principles for intrafinger and interfinger coupling-compliance mechanisms are developed, enabling the mechanical embodiment of joint kinematic synergies within and between fingers. Furthermore, a design principle for anthropomorphic robotic hands that reduces actuator count while retaining diverse motion functions is formulated. This principle is embodied in a 12-actuator, 21-joint prototype named SX-Hand, featuring a flexible thumb and an articulated palm. Experimental evaluations demonstrate that SX-Hand achieves the maximum score in the Kapandji test, performs all 33 grasp types defined in the comprehensive GRASP taxonomy, and executes dexterous in-hand manipulations. The proposed approach provides an effective conceptual and technical route for reproducing rich motion functions with reduced actuation, offering valuable guidance for the development of efficient anthropomorphic robotic hands and other bionic systems.
This study presents and experimentally validates an adaptive control method for human-exoskeleton interaction through online adaptation of desired joint trajectories. Leveraging gait phase and human-exoskeleton interaction torque estimators, our approach enables seamless assistance adaptation to varying walking patterns and speeds. Specifically, a pretrained neural network approximates the exoskeleton's dynamics, enabling real-time interaction torque estimation from kinematic measurements and commanded motor torques alone. These estimates drive a gradient-descent update of the joint reference trajectories, minimizing a cost function that penalizes both interaction torques and trajectory modification, ensuring bounded convergence and stability without user-specific parameter tuning. We compared our adaptive controller with a fixed-trajectory gait-phase-based controller during overground and treadmill walking at three self-selected speeds ranging from 0.4 to 0.8 m/s. In 16 participants, the adaptive controller significantly reduced the hip and knee interaction torques by 51.2% +/- 11.1 and 63.9% +/- 29.7, respectively, during over-ground walking. Muscular effort significantly decreased in Bicep Femoris (21.0% +/- 34.5) and Rectus Femoris (28.1% +/- 34.6), while remaining unchanged in other muscles. Cadence and gait speed increased by 7.6% +/- 5.2 and 10.7% +/- 8.3, respectively, indicating that participants could walk faster with less effort due to trajectory adaptation. Postadaptation trajectories more closely resembled those of walking without the exoskeleton, and exoskeleton-torques aligned more closely with human biological torques. Our proposed adaptive controller, which requires only exoskeleton kinematics, also maintained performance during treadmill walking across speeds, demonstrating speed-invariant behavior compared to the nonadaptive controller.
This study introduces a novel mobile robot inspired by the pill bug that offers dual locomotion modes, shape morphing, and protective sliding curved shells. The proposed design includes two primary mobility modes: walking and active rolling, providing flexibility and adaptability to navigate various terrains. Through a systematic approach involving type and dimensional synthesis, an underlying morphing mechanism is created to replicate the distinctive shapes of a pill bug's body. This mechanism features a single-input-multiple-output structure designed using multiloop coupled mechanisms that mimic the natural body motion of pill bugs. To ensure surface protection while allowing relative sliding between shell segments, curved shells are attached to the tracer points of the mechanism. For active switching between rolling and walking modes, two adaptive one-degree-of-freedom legs are integrated into the morphing mechanism. By manipulating speed differences between these legs, steering capabilities are achieved to ensure smooth transitions between locomotion modes.
Despite decades of research in magnetic resonance imaging (MRI)-compatible robotic technologies, the existing MRI-safe needle drivers rarely feature simultaneously high compactness, large insertion force, and motion versatility, all of which are critical to facilitate clinical translation in intraoperative MRI-guided percutaneous procedures. The paper presents an MR-safe needle driver that for the first time offers all these desired qualities. It measures only 2.2 × 5.3 × 3.8 cm (length × width × height), facilitating its adoption in in-bore skull-mounted or body-mounted MRI-guided procedures. It is driven by a single hydraulic bellows-based actuator, which provides good water sealing, smooth motion, and high expansion ratio, and a pre-clamped gripper design that offers large insertion force (>10 N). A compact passive rotation mechanism, together with a motion decoupling and switching mechanism, was introduced, allowing the needle to move with three motion types: independent translation, translation with passive rotation, and independent rotation. The passive rotation motion reduces needle deformation and tissue resistance during insertion, while the combination of independent tran
Learning from demonstration is a powerful method for robotic skill acquisition. Nevertheless, a critical limitation lies in the substantial costs associated with gathering demonstration datasets, typically action-labeled robot data, which create a fundamental constraint in the field. Video data offer a compelling solution as an alternative rich data source, containing diverse behavioral and physical knowledge. This study introduces G3M, an innovative framework that exploits video data via Graph-to-Graphs Generative Modeling, which pretrains models to generate future graphs conditioned on the graph within a video frame. The proposed G3M abstracts video frame into graph representations by identifying object and visual action vertices for capturing state information. It then effectively models internal structures and spatial relationships present in these graph constructions, with the objective of predicting forthcoming graphs. The generated graphs function as conditional inputs that guide the control policy in determining robotic behaviors. This concise method effectively encodes critical spatial relationships while facilitating accurate prediction of subsequent graph sequences, thus allowing the development of resilient control policy despite constraints in action-annotated training samples. Furthermore, these transferable graph representations enable the effective extraction of manipulation knowledge through human videos as well as recordings from robots with different embodiments. The experimental results demonstrate that G3M attains superior performance using merely 20% action-labeled data relative to comparable approaches. Moreover, our method outperforms the state-of-the-art method, showing performance gains exceeding 19% in simulated environments and 23% in real-world experiments, while delivering improvements of over 35% in cross-embodiment transfer experiments and exhibiting strong performance on long-horizon tasks.
This paper explores the challenge of optimal routing for a mobile robot navigating a dynamic and shared human environment. The primary goal is to minimize the risk of performance degradation during motion, such as delays in completing tasks due to the need for safe or acceptable human robot encounters. The problem is formulated as a graph whose edge costs become progressively known only as the robot moves through the environment. We model this problem as a Markov Decision Process (MDP), enabling an offline evaluation of the expected cost of alternative routes based on statistical information about human spatial distributions and possible observations at each intersection. This compact state representation scales linearly with the number of intersections in the map. Since the memoryless property of the MDP may induce loops during online execution, we compute an offline policy and introduce an online policy adaptation mechanism to prevent cyclic behaviors. Exten sive simulations across environments of different complexity, and using data collected from real-world experiments, demonstrate that our approach outperforms reactive and advanced state-of the-art planners in terms of either performance or scalability.
Perception in granular media remains challenging due to unpredictable particle dynamics. To address this challenge, we present SandWorm, a biomimetic screwactuated robot augmented by peristaltic motion to enhance locomotion, and sandworm tactile sensor (SWTac), a novel event-based visuotactile sensor with an actively vibrated elastomer. The event camera is mechanically decoupled from vibrations by a spring isolation mechanism, enabling high-quality tactile imaging of both dynamic and stationary objects. For algorithm design, we propose an IMU-guided temporal filter to enhance imaging consistency, improving masked signal-to-noise ratio (MSNR) by 24%. Moreover, we systematically optimize SWTac with vibration parameters, event camera settings, and elastomer properties. Motivated by asymmetric edge features, we also implement contact surface estimation by U-Net. Experimental validation demonstrates SWTac's 0.2 mm texture resolution, 98% stone classification accuracy, and 0.15 N force estimation error, while SandWorm demonstrates versatile locomotion (up to 12.5 mm/s) in challenging terrains, successfully executes pipeline dredging and subsurface exploration in complex granular media (observed 90% success rate). Field experiments further confirm the system's practical performance.
This paper presents a hierarchical control framework for quadrupedal locomotion that unifies the complementary strengths of model-based optimization and reinforcement learning. We develop a convex Quadratic Programming~(QP) solver based on the primal-dual Chambolle-Pock algorithm, enabling both massively parallel policy training and real-time deployment through efficient handling of constrained optimization problems. Our hierarchical framework employs learned policies for robust high-level control to handle real-world perturbations, while ensuring safety and energy efficiency through a low-level whole-body controller powered by the proposed solver. Extensive benchmarks and experimental validation demonstrate quantifiable improvements in energy consumption, constraint satisfaction, and task transferability across simulated and real-world environments.
Current numerical methods for solving kinematic identification (KI) and inverse kinematics (IK) are limited in accuracy, convergence rates, and robustness, necessitating further enhancement. This article presents a modified Halley method for solving the KI and IK problems of serial robots based on the product of exponentials formula, achieving quintic convergence. Specifically, a general error model is first established based on exponential coordinates, and KI and IK are reformulated as root-finding problems. Next, the modified Halley method, which is proven to be a fifth-order method and incorporates a damping strategy, is proposed to resolve the singularity issue and enhance robustness. Subsequently, the Jacobian and Hessian matrices required for the proposed method are analytically derived based on the time differential of exponentials. Furthermore, highly simplified and explicit formulas for these matrices are presented for the IK problem. Simulations on serial robots with various configurations validate the proposed method's accuracy, convergence rates, and robustness in solving KI and IK problems, as well as its advantages over the state of the art. In addition, experimental validation of KI on two physical robots further demonstrates the effectiveness of the proposed method. Our custom-written MATLAB and C++ codebases are made publicly available for download.
Due to their continuous electromechanical deformation, rate-dependent viscoelasticity, and complex mechanical vibration, dynamic modeling and high-speed tracking control of dielectric elastomer actuators (DEAs) remain elusive, significantly limiting their working bandwidth. In this work, we propose a physics-informed token prediction (PITP) that enables accurate modeling of DEA dynamics and high-speed feedforward tracking control. The PITP framework consists of two key components: a physics-informed encoder and a dynamic decoder. The physics-informed encoder is designed based on a simplified equivalent linear model and trained through the hierarchical optimization training method, which embeds the global dynamic characteristics into tokens, minimizing the need for extensive data and training. Then, the dynamic decoder is developed by using these tokens as state-dependent parameters, capable of describing complex dynamic responses through the autoregressive solution. Finally, by taking advantage of the model's reversibility, a direct inverse compensator is established to linearize the input-output relationship. Experimental results of several DEAs with different configurations and payloads demonstrate that, based on our PITP framework, the complex nonlinear dynamic responses of all DEAs can be precisely described and eliminated within their natural frequency, validating its generality and versatility. By leveraging fast modeling (< 30 min) and high-speed feedforward tracking control, our PITP framework may accelerate DEAs' practical applications.
Soft robots' ability to safely navigate complex environments motivates the development of algorithms for accurate environmental interaction assessment, enabling greater autonomy. Specifically, strain-based shape and force estimation of continuum robots with embedded soft sensors poses an open challenge mainly owing to continuous softness, anisotropic deformation, and nonlinear properties. Mathematical description of deformable soft bodies and accurate estimation of external forces are crucial for achieving controllable and intelligent behaviors of these robots. In this article, a kinetostatic strain-based modeling for rod-driven soft robots with embedded stretch sensors is proposed, which incorporates local strains, actuation variables, and external interactions. The strain model enables full shape estimation of the robot and prediction of strain variations in soft bodies. Building on this, we develop a force estimator based on predicted and measured sensor and actuator lengths to evaluate 3-D external forces, accounting for both orthogonal and tangential components relative to the backbone. Moreover, we introduce a methodology using a novel ellipsoid representation to handle tangential forces that may become insensitive in certain singular configurations. This estimator allows us to either disregard such forces when they do not influence deformation or estimate them when they become observable. Our simulations and experiments demonstrate how this approach can be used to analyze the robot's configuration and successfully estimate external forces. Finally, it is demonstrated that when the continuum arm follows trajectories with higher strain sensitivity, tangential force estimation is significantly improved.
We propose a novel initialization and online spatial-temporal calibration method for visual-inertial odometry (VIO), which decouples rotation and translation estimation to achieve higher accuracy and better robustness. Existing initialization methods suffer from limited accuracy or robustness (e.g., in scenarios with small translational motion) and rarely integrate simultaneous spatial-temporal calibration during initialization, despite its considerable practical value. Our proposed method leverages rotation-translation decoupling constraints to enable simultaneous estimation of gyroscope bias, extrinsic rotation, and camera-inertial measurement unit (IMU) time offset-even under pure rotational motion. Moreover, we are the first to conduct observability analysis on rotational constraints in rotation-translation decoupling methods, experimentally identifying the unobservable state-space directions under three degenerate motions within our approach. We also perform extensive experiments to delineate practical parameter solution boundaries for our method, with both efforts substantially enhancing the overall practical applicability of decoupling-based methods. Extensive experiments on simulated and real-world datasets demonstrate that our method outperforms state-of-the-art approaches in accuracy and robustness while maintaining computational efficiency. Furthermore, experiments verify that it significantly improves convergence in VIO systems.
A deformable, flexible sheet that is held by multiple robots can be used for manipulating and transporting an object. For such multirobot collaborative transporting system, forward kinematics provide object's potential positions where it might remain stationary on the sheet. Some of these forward kinematics solutions are, however, demonstrated unstable under object's position or robot formation perturbations. This article presents stability criteria of the kinematics solutions of the object on the deformable sheet held by multirobot system. We capture the sheet deformation and object position using the virtual variable cable model. A constrained quadratic problem is formulated to obtain the forward kinematics solution for a given multirobot configuration. Linear dependence of active constraints and stability multipliers are used to assess the stability of the kinematics solutions. Two stability criteria are proposed and analyzed under object position or robot formation perturbations. These criteria are related to the properties and conditions of the stability multipliers. We present an efficient computational algorithm to determine stable kinematics, which are a small fraction of feasible solutions. Experimental results and case studies are presented to validate and demonstrate the effectiveness and efficiency of the analyses and algorithms.
This article describes a solution for pick-and-place tasks that do not require high precision throughout the execution, using a robotized gantry crane. Two common issues that occur when using a crane are addressed, and solutions are provided. First, the lack of rotational controllability is overcome by designing a gripper that passively aligns itself with the handle of the payload using a simple, robust control strategy. Second, the active workspace is expanded by using controlled, dynamic motions, based on a variable-length pendulum model. Thus, the workspace is no longer limited to positions directly accessible from above, as is the case with quasi-static control methods. The robustness and effectiveness of the proposed solution was validated by a grasping, and a shelf insertion experiment. The robot was able to grasp the payload during all 48 trials. Failures were detected and recovery strategies were implemented. The handle could also be reliably ungrasped. During the shelf insertion, imperfect trajectory tracking caused a significant error during the unobservable part of the trajectory. Nevertheless, the actual placement position was always close to the desired position.
This article investigates the reliable navigation problem, which requires that the ego vehicle navigates itself to the destination with the maximized stochastic on-time arrival (SOTA) probability in a given uncertain topological transportation network. One distinctive characteristic of the SOTA problem explored in this article is the inherent uncertainty stemming from the underlying network's topology, i.e., some of the edges might become untraversable during navigation. To the best of our knowledge, almost all conventional SOTA solutions presume that the network topology remains the same during the ego vehicle's navigation process. However, when facing uncertainties in the network's topology, these algorithms may experience a significant performance degradation. To address the challenge of uncertain network topology, we first formulate the special reliable navigation problem into the variational Markov decision process framework, and then initiate a new reinforcement learning-based algorithm, namely sample efficient variational actor critic (SEVAC) as its solution. SEVAC comprises the variational policy gradient (VPG) module, which optimizes the vehicle's routing policy, and the masked temporal difference (MTD) module, which approximates the underlying routing policy's SOTA probability. Both modules are extended to their off-policy counterparts, namely off-policy VPG and off-policy MTD, to improve the algorithm's sample efficiency. SEVAC are compared with several conventional SOTA solutions as well as Canadian traveller problem algorithms in a variety of commonly used transportation test networks, and achieve the best overall SOTA performance. Furthermore, we validate SEVAC's application to real-world scenarios by navigating a physical robot in a self-constructed indoor environment as well as a real-world building environment with uncertain topology.
