Dual-arm robotic manipulation of liquid or biochemical reagents poses critical challenges due to high-dimensional configuration spaces, stringent task-specific end-effector orientation requirements to prevent spillage, frequent inter-arm collisions, and cluttered experimental environments. This paper introduces CLEAR-MP (Clearance Learning-Based Efficient Motion Planning for Dual-Arm Robots under End-Effector Orientation Constraints), a modular framework that integrates multiple innovations: a decoupled learning-driven collision estimation module–comprising aPairwise Link Clearance Networkfor self-collision and aClearance Inference Networkfor environmental obstacles, aLearning-Driven Bidirectional Parallel Search Strategyfor accelerated tree expansion, parallel Cartesian batch sampling for efficient candidate generation, fast inverse-kinematics mapping, andLearning-Guided Batch Shortcut Optimizationto refine trajectories. Together, these components generate smooth, safety-certified paths with substantially reduced planning time and path length. Extensive simulations and real-robot experiments show that CLEAR-MP achieves an average path length of 2.391 m, average planning time of 3.529 s, outperforming state-of-the-art baselines by over 50% in computation and 40% in trajectory quality while maintaining strong generalization without retraining.

The rapid advancement of humanoid robotics has highlighted the challenge of coordinating high-redundancy whole-body motion and posture control under multiple safety constraints. The difficulty lies particularly in dual-arm task planning and the design of high-precision, real-time control strategies. To address these issues, this paper proposes a novel neural-network (NN) based real-time model predictive control (MPC) framework for humanoid robots. The framework innovatively integrates discrete recurrent neural networks (DRNN) with MPC, thereby extending their combined advantages to high-redundancy humanoid motion control. In addition, a primal-dual neural network (PDNN) solver is employed to compute multi-constrained kinematic MPC in a single iteration, avoiding the repeated iterations required in conventional MPC and significantly enhancing real-time performance. The proposed approach is rigorously validated through theoretical derivation and extensive experiments, including both numerical simulations and real-world trials. After theoretical verification in simulation, the NN-based MPC framework is deployed on a self-developed humanoid robotic platform. Experimental results confirm that the NN-based MPC method achieves effective and highly accurate whole-body task planning and real-time control, demonstrating its potential as a reliable solution for advanced humanoid robot control.

Deformable object manipulation in robotics presents significant challenges due to uncertainties in component properties, diverse configurations, visual interference, and ambiguous prompts. These factors complicate both perception and control tasks. To address these challenges, we propose a novel method for One-Shot Affordance Grounding of Deformable Objects (OS-AGDO) in egocentric organizing scenes, enabling robots to recognize previously unseen deformable objects with varying colors and shapes using minimal samples. Specifically, we first introduce the Deformable Object Semantic Enhancement Module (DefoSEM), which enhances hierarchical understanding of the internal structure and improves the ability to accurately identify local features, even under conditions of weak component information. Next, we propose the ORB-Enhanced Keypoint Fusion Module (OEKFM), which optimizes feature extraction of key components by leveraging geometric constraints and improves adaptability to diversity and visual interference. Additionally, we propose an instance-conditional prompt based on image data and task context, which effectively mitigates the issue of region ambiguity caused by prompt words. To validate these methods, we construct a diverse real-world dataset, AGDDO15, which includes 15 common types of deformable objects and their associated organizational actions. Experimental results demonstrate that our approach significantly outperforms state-of-the-art methods, achieving improvements of 6.2%, 3.2%, and 2.9% in KLD, SIM, and NSS metrics, respectively, while exhibiting high generalization performance. Source code and benchmark dataset are made publicly available at https://github.com/Dikay1/OS-AGDO.

In contact-rich tasks such as polishing and drilling, inevitable physical interactions often lead to task deviations due to interference, typically resulting in excessive contact forces and eventual task failure. To tackle these challenges, we propose an innovative human-guided visual-impedance control framework. Specifically, we first introduce an interaction model within image feature space, which models the dynamics of human-robot-environment interactions. Subsequently, human operation skills are characterized through human-guided wrenches, and acts on visual features through a projection matrix, thus integrating human-guided wrenches with visual-impedance interaction dynamics. Finally, leveraging this framework, we develop a novel variable-stiffness visual-impedance control strategy. The impedance parameters are optimized online via Quadratic Program, ensuring that the end-tool contact force converges to desired value while adhering to safety constraints. The validity of the proposed framework was established through polish experiments.

Since its inception, impedance control has emerged as a fundamental framework for robotic interaction control. Recent advancements in geometric impedance control have demonstrated certain advantages over traditional Cartesian impedance control. However, existing geometric impedance control approaches generally lack force regulation capabilities or rigorous stability guarantees. In this paper, we propose a safety-aware geometric force-impedance controller that addresses these limitations. By incorporating an energy tank mechanism, the proposed approach enables precise force tracking while preserving full compatibility with the impedance behavior. Furthermore, an energy injection and freezing mechanism is introduced, allowing dynamic regulation of energy exchange between the tank and the robotic system. Notably, the proposed method eliminates the need for an offline estimation of the initial energy stored in the tank, facilitating real-time adjustments of force controller parameters. To validate the effectiveness of the proposed framework, we conduct extensive polishing experiments on a real robotic platform. The results demonstrate the capability of the proposed controller to achieve stable and precise force regulation.

Aiming at the area search task of a multi-robot system in an unknown complex obstacle environment, we propose a cooperative area search algorithm based on a dual improved bio-inspired neural network (DIBNN). First, we improve the BNN model to reduce the interference of the complex obstacle environment on robot decision making. Each robot generally chooses the neuron with the largest sum of surrounding activity values among adjacent neurons as its next movement position. Then, we propose a collaborative search mechanism. When a robot falls into a local deadlock state in the complex obstacle environment, the mechanism will guide the robot to quickly find unsearched areas. Finally, we conduct multi-robot area search simulation experiments under different obstacle environments and compare them with three baseline algorithms in this field. The simulation results verify that the proposed algorithm can efficiently guide the multi-robot to complete the area search task in the complex obstacle environment.Note to Practitioners—The motivation of this article arises from the need to develop fast and effective area search algorithms for practical applications such as UAV swarm reconnaissance and multiple mobile robots area search and rescue. The algorithms based on BNN has been widely used in search tasks under unknown environments due to its good scalability and efficiency. However, the efficiency of area search in complex obstacle environments cannot be guaranteed. In order to achieve efficient area search in unknown complex obstacle environments, the DIBNN algorithm is proposed. It utilizes a cooperative search mechanism and achieves better performance. DIBNN can also be applied to multi-robot systems in different scenarios, demonstrating strong scalability.

Physical human-robot interaction (pHRI) plays an important role in robotic. In order for a human operator to be able to easily adapt to interact with a robot, a minimal interaction force in pHRI should be achieved. In this paper, a pHRI framework is proposed to allow the robot to regulate its trajectory adaptively for minimizing the interaction force with small position-tracking errors. The trajectory of the robot is first adjusted by the interaction force which is updated by the performance evaluation index. Then, the human hand motion is predicted based on the autoregressive (AR) model to further adapt the trajectory. Thirdly, an adaptive impedance control method is developed to update the stiffness in the robot impedance controller using surface electromyography (sEMG) signals for robot compliant interaction with the environment. This method allows the human operator to interact with the robot by the interaction force, the hand motion and muscle contraction. By investigating the performance of the proposed method, the interaction force is decreased and a good position tracking accuracy is achieved. Comparative experiments demonstrate the enhanced performance of the proposed method. Note to Practitioners—This paper focuses on developing a novel method that can allow the robot to compliantly interact with the human operator while simultaneously taking into account the trajectory-tracking accuracy and the interaction force in pHRI scenarios. The proposed method has a large application potential in a variety of pHRI tasks, such as human-robot collaborative transporting, curing, assembly, cutting, and so on. In addition, the proposed method can allow the human operator to physically interact with the robot in an easier and more intuitive manner, by taking advantage of human motion prediction and adaptive impedance control. Therefore, it is also potentially utilized for rehabilitation and assistive robots, and robot learning skills from human physical demonstration.

Existing cooperative manipulation methods for multiple manipulator systems usually assume that the grasp matrix and the desired trajectory of each manipulator are known in advance. In this work, distributed neural adaptive impedance control (AIC) strategies integrating fully distributed observers are proposed to remove both limitations. Specifically, two fully distributed finite-time observers are designed to estimate the actual and ideal states of the reference point without using global information. The estimates of the grasp matrix and the desired trajectory of each end-effector (EE) are then obtained by kinematic constraints and the estimates of the reference point's states. At the controller development, a distributed adaptive impedance model is established to achieve an adaptive trade-off between tracking performance and compliance. Then, distributed neural network (NN)-based tracking control strategies are developed to asymptotically realize the desired adaptive impedance dynamics in the presence of uncertainties. Additionally, a virtual energy tank (EK) is employed to interact with the impedance system to correct the adaptive impedance laws for system passivity. A simulation for four mobile manipulators tightly cooperative transport an unknown object is carried out to demonstrate the established results.

This article introduces CIMAP, a high-performance motion planning algorithm for robotic manipulators in complex environments, based on the clearance inference network (CIN). CIMAP incorporates a batch collision estimation module powered by CIN, which efficiently predicts collisions by dividing the manipulator’s workspace into voxels and estimating clearances between the manipulator and surrounding obstacles. The algorithm also features a batch adaptive bidirectional expansion mechanism, enabling the simultaneous extension of multiple nodes within joint space. Leveraging CIN for batch collision estimation, CIMAP accelerates the discovery of feasible paths. Additionally, CIMAP includes a phased path optimization mechanism that identifies local shortcuts through CIN, improving path efficiency. A geometric collision checker ensures safety, performing necessary repairs when required. To assess CIMAP’s effectiveness in continuous motion planning, we compared its performance against four existing algorithms (CN-RRT, B-RRT, GB-RRT*, and NPB-RRT*-DC) across various obstacle scenarios. Experimental results demonstrate that CIMAP achieves an average motion planning time of under 0.7 s, improving planning efficiency by at least 89% compared to the baseline algorithms, while maintaining shorter path lengths.

Free-form surfaces have been widely used in industrial design and manufacturing. For the requirements of measurement efficiency and precision, robots and optical scanners are applied to measure free-form surface parts increasingly. Due to the complex geometry shapes and occlusions of these parts, how to plan accessible viewpoints of a scanner to achieve the expected coverage rate is a challenging task. This paper presents a novel viewpoint planning method based on the visibility cone space explorer (VP-VCSE) for robotic measurement systems with 7 degrees of freedom (7-DOF). A digital twin for the robotic measurement system is implemented to provide core services for robotic measurement tasks, including sensor simulation and collision detection. To generate initial candidate viewpoints, a novel mesh segmentation algorithm based on the hybrid mixture model is proposed, which is convenient to handle the triangular mesh of the target object. Visibility computation for a target object in given viewpoints is the key to dealing with the occlusion problem. For this purpose, a general visibility model of a structured-light scanner is presented to compute visible areas accurately. In order to reduce occlusions, a visibility cone space explorer is designed to search optimal candidate viewpoints considering inverse kinematics and physical collisions simultaneously. The viewpoint planning problem is formulated as a set covering optimization problem and a next-best-view operator is introduced to improve the efficiency of the genetic algorithm for searching the resultant viewpoint set, guaranteeing the expected coverage rate and data overlap rate. The simulation and experiment results for four different test models show that the proposed algorithm outperforms the existing methods in terms of the uncovered rate and the minimum number of viewpoints.Note to Practitioners—This paper addressed a viewpoint planning problem for the robotic measurement system with a binocular structured light 3D scanner mounted on the end effector of the robot, where a robot and a turntable cooperate to complete the measurement tasks. The goal is to find a minimal number of viewpoints that provides full coverage of the target surfaces. Although many studies have addressed this problem, there is little discussion about strategies to improve coverage rate when the target object has complex occlusions. This paper suggested a valuable practice to construct a visibility cone space to adjust viewpoint to reduce occlusions and improve the overall coverage rate. Simulation and experimental results demonstrated the feasibility and effectiveness of the proposed approach. This paper showed how to deal with various constraints that a feasible viewpoint needs to satisfy in the viewpoint generation, viewpoint adjustment, and viewpoint selection phase. Moreover, this paper provided a solution for developing the visualization, simulation, and interaction of a digital twin for the 7-DOF robotic measurement system. All core services for robotic measurement tasks are implemented based on a set of open source libraries, which provides a convenient learning and research software platform for practitioners. In future research, we will study how to improve the intelligence and cooperation of the robotic measurement system through deep learning or reinforcement learning techniques.

In this paper, considering the difficulty of the interaction between the multi-mobile manipulators and the environment, the dynamics model of the mobile manipulator is analyzed, and a hybrid force/position control method based on the prescribed performance is proposed to improve the stability of the multi-mobile manipulators in the process of cooperative object transportation. Firstly, the dynamics model of the underdriven system of the multi-mobile manipulators is established by the Newton-Euler theorem. Then, the equivalent control theory is adopted for underdriven system, and a prescribed performance control method is proposed by considering the motion interference between the mobile manipulator and the high precision control of the manipulator. At the same time, an adaptive impedance control method is used to overcome internal and external disturbance during the cooperative transport of multi-mobile manipulators. The stability of the proposed method is analyzed through the Lyapunov stability theory. Finally, the effectiveness and superiority of the proposed scheme are verified through a simulation of multi-mobile manipulators collaborative object transportation.

Visual servoing technology has widely been employed in manufacturing because it is a flexible, realizability, and low-cost way to improve the intelligence of the industry robot. Nevertheless, a worrisome and overlooked issue is that the loss of visual features in the camera's field of view may lead to the failures of the visual servoing tasks. This article addresses the visual features escaping problem, by implementing an asymmetric barrier Lyapunov function with a field-of-view constraint controller. The asymmetric barrier Lyapunov function defines a tightly specified range for the feature coordinate errors and ensures the transient response of the tracking error as well as enables arbitrary tracking accuracy. It is worth noting that the asymmetric barrier Lyapunov function directly handles the visual-robot-coupled dynamics while guaranteeing system stabilities. Besides, to accommodate the uncertain dynamics derived from a high-dimensional coupled system, an adaptive controller is proposed utilizing fuzzy neural networks with computational efficiency and few training parameters to enhance the control performance. Finally, the effectiveness of the proposed control strategy has been demonstrated through both theoretical analysis and experimental verification.

Due to ability to estimate the spatial transformation of coordinate frames, point cloud registration is a fundamental technique in manufacturing. Previous methods prone to converge to wrong local minima, in the cases of large initialization, noise, outliers, and partiality. This article presents a new learning-based robust point cloud registration approach to predict a rigid transformation in a one-shot way. Our network aims to determine a matchability matrix to yield an accurate registration result. Each element of the matchability matrix refers to similarity of learned per-point embeddings and represents the probability of a potential correspondence. The following two major blocks are developed to guide the matchability matrix to represent correct correspondences: an attention block is introduced to enhance the discriminativeness of learned per-point embeddings, and a zero-mean Gaussian-based annealing layer and a differentiable Sinkhorn normalization layer are designed to enforce a permutation matchability matrix. With the matchability matrix, an intuitive solution is integrated to obtain the relative transformation of the source and target point clouds. Different from the existing work, our network can handle partially overlapped point-cloud pairs effectively. Experimental results demonstrate the superiority of the proposed approach over the state-of-the-art registration approaches in terms of accuracy and robustness.

Fault detection for distributed parameter systems (DPSs) generally requires the complete model information to be known so far. However, for numerous industrial applications, it is common that accurate first-principles physical models are extremely difficult to obtain. Hence, the applicability of traditional model-based methods is being restricted. To pave the way, an adaptive neural network (AdNN) is constructed to simultaneously estimate the state variable and the unknown nonlinearity for a class of partially known nonlinear DPSs. Moreover, considering that full-state measurement is unrealistic in applications, the proposed adaptive neural observer is based on a reduced-order model, which also increases the computation efficiency. Then, the residual generation and evaluation are conducted using the output estimation error of the proposed adaptive neural observer. Bearing the effects of the neglected fast dynamics in mind, a data-driven threshold generation scheme is proposed. Extensive experimental results are presented and analyzed to validate the effectiveness of the proposed method.

In this article, an adaptive fixed-time fuzzy control scheme is proposed for an uncertain robot manipulator with user-defined performance. A novel symmetrical barrier Lyapunov function is designed based on the error conversion mechanism and the performance function such that the tracking errors will not violate the prescribed output constraints. A novel adaptive law is constructed and incorporated into the fixed-time controller design such that all the closed-loop signals can be bounded and achieve practical fixed-time convergence regardless of the initial conditions. Finally, the feasibility and superiority of the proposed scheme are demonstrated based on simulation and experimental studies using a Baxter robot.

This article presents an adaptive control method for dual-arm robot systems to perform bimanual tasks under modeling uncertainties. Different from the traditional symmetric bimanual robot control, we study the dual-arm robot control with relative motions between robotic arms and a grasped object. The robot system is first divided into two subsystems: a settled manipulator system and a tool-used manipulator system. Then, a command filtered control technique is developed for trajectory tracking and contact force control. In addition, to deal with the inevitable dynamic uncertainties, a radial basis function neural network (RBFNN) is employed for the robot, with a novel composite learning law to update the NN weights. The composite learning is mainly based on an integration of the historic data of NN regression such that information of the estimate error can be utilized to improve the convergence. Moreover, a partial persistent excitation condition is employed to ensure estimation convergence. The stability analysis is performed by using the Lyapunov theorem. Numerical simulation results demonstrate the validity of the proposed control and learning algorithm.

In this work, we focus on improving the robot’s dexterous capability by exploiting visual sensing and adaptive force control. TeachNet, a vision-based teleoperation learning framework, is exploited to map human hand postures to a multi-fingered robot hand. We augment TeachNet, which is originally based on an imprecise kinematic mapping and position-only servoing, with a biomimetic learning-based compliance control algorithm for dexterous manipulation tasks. This compliance controller takes the mapped robotic joint angles from TeachNet as the desired goal, computes the desired joint torques. It is derived from a computational model of the biomimetic control strategy in human motor learning, which allows adapting the control variables (impedance and feedforward force) online during the execution of the reference joint angle trajectories. The simultaneous adaptation of the impedance and feedforward profiles enables the robot to interact with the environment in a compliant manner. Our approach has been verified in multiple tasks in physics simulation, i.e., grasping, opening-a-door, turning-a-cap, and touching-a-mouse, and has shown more reliable performances than the existing position control and the fixed-gain-based force control approaches.

Due to strongly coupled nonlinearities of the grasped dual-arm robot and the internal forces generated by grasped objects, the dual-arm robot control with uncertain kinematics and dynamics raises a challenging problem. In this paper, an adaptive fuzzy control scheme is developed for a dual-arm robot, where an approximate Jacobian matrix is applied to address the uncertain kinematic control, while a decentralized fuzzy logic controller is constructed to compensate for uncertain dynamics of the robotic arms and the manipulated object. Also, a novel finite-time convergence parameter adaptation technique is developed for the estimation of kinematic parameters and fuzzy logic weights, such that the estimation can be guaranteed to converge to small neighborhoods around their ideal values in a finite time. Moreover, a partial persistent excitation property of the Gaussian-membership-based fuzzy basis function was established to relax the conventional persistent excitation condition. This enables a designer to reuse these learned weight values in the future without relearning. Extensive simulation studies have been carried out using a dual-arm robot to illustrate the effectiveness of the proposed approach.

Robots with coordinated dual arms are able to perform more complicated tasks that a single manipulator could hardly achieve. However, more rigorous motion precision is required to guarantee effective cooperation between the dual arms, especially when they grasp a common object. In this case, the internal forces applied on the object must also be considered in addition to the external forces. Therefore, a prescribed tracking performance at both transient and steady states is first specified, and then, a controller is synthesized to rigorously guarantee the specified motion performance. In the presence of unknown dynamics of both the robot arms and the manipulated object, the neural network approximation technique is employed to compensate for uncertainties. In order to extend the semiglobal stability achieved by conventional neural control to global stability, a switching mechanism is integrated into the control design. Effectiveness of the proposed control design has been shown through experiments carried out on the Baxter Robot.