Dual-arm humanoid robots are poised to transform industrial manufacturing automation in human-centric environments. However, unlocking this potential requires a unified framework that can simultaneously handle coupled bimanual coordination, versatile physical interaction, and safety. We introduce Passive Model Predictive Cooperative Interaction Control (P-MPCIC), a framework that co-optimizes task performance and interaction safety under a formal passivity guarantee. P-MPCIC integrates model predictive control for the bimanual subsystem within a whole-body architecture and uses a coupling matrix to enforce synchronization objectives across relative motion and force distribution. For interaction prediction, the framework incorporates a composite robot-environment model that combines parallel and series impedance dynamics, yielding a linear state-space predictor. Passivity is enforced as a constraint on the energy balance at the interaction port, preventing destabilizing energy generation from the controller. We verify the framework’s core principles through planar simulations and demonstrate its practical effectiveness on a 7-DoF dual-arm humanoid.

This paper introduces an upper limb postural optimization method for enhancing physical ergonomics and force manipulability during bimanual human-robot co-carrying tasks. Existing research typically emphasizes human safety or manipulative efficiency, whereas our proposed method uniquely integrates both aspects to strengthen collaboration across diverse conditions (e.g., different grasping postures of humans, and different shapes of objects). Specifically, the joint angles of a simplified human skeleton model are optimized by minimizing the cost function to prioritize safety and manipulative capability. To guide humans towards the optimized posture, the reference end-effector poses of the robot are generated through a transformation module. A bimanual model predictive impedance controller (MPIC) is proposed for our human-like robot, CURI, to recalibrate the end effector poses through planned trajectories. The proposed method has been validated through various subjects and objects during human-human collaboration (HHC) and human-robot collaboration (HRC). The experimental results demonstrate significant improvement in muscle conditions by comparing the activation of target muscles before and after optimization.

Recent work has demonstrated the potential of diffusion models in robot bimanual skill learning. However, existing methods ignore the learning of posture-dependent task features, which are crucial for adapting dual-arm configurations to meet specific force and velocity requirements in dexterous bimanual manipulation. To address this limitation, we propose Manipulability-Aware Diffusion Policy (ManiDP), a novel imitation learning method that not only generates plausible bimanual trajectories, but also optimizes dual-arm configurations to better satisfy posture-dependent task requirements. ManiDP achieves this by extracting bimanual manipulability from expert demonstrations and encoding the encapsulated posture features using Riemannian-based probabilistic models. These encoded posture features are then incorporated into a conditional diffusion process to guide the generation of task-compatible bimanual motion sequences. We evaluate ManiDP on six real-world bimanual tasks, where the experimental results demonstrate a 39.33% increase in average manipulation success rate and a 0.45 improvement in task compatibility compared to baseline methods. This work highlights the importance of integrating posture-relevant robotic priors into bimanual skill diffusion to enable human-like adaptability and dexterity.

This paper proposes a novel whole-body impedance control method for the Collaborative dUal-arm Robot manIpulator (CURI) in Human-Robot Collaboration (HRC). The method enables CURI to adapt its physical behavior to human motion while following trajectories learned from human-human demonstrations. A whole-body impedance controller coordinates the robot joints to achieve desired Cartesian space impedance. Collaborative tasks are captured from human-human demonstrations and represented using a Task-parameterized Gaussian Mixture Model (TP-GMM). Electromyography (EMG) sensors record muscle activities to estimate human impedance profiles, which are then mimicked by a variable impedance controller. An adaptive parameter is introduced to adjust robot stiffness based on spatial displacement between the robot and human, ensuring safe and efficient interaction. Experimental validation through confrontational Tai Chi pulling/pushing tasks demonstrates the superiority of the proposed adaptive impedance method over the fixed impedance controller.

Open-world task planning, characterized by handling unstructured and dynamic environments, has been increasingly explored to integrate with long-horizon robotic manipulation tasks. However, existing evaluations of the capabilities of these planners primarily focus on single-arm systems in structured scenarios with limited skill primitives, which is insufficient for numerous bimanual dexterous manipulation scenarios prevalent in the real world. To this end, we introduce OBiMan-Bench, a large-scale benchmark designed to rigorously evaluate open-world planning capabilities in bimanual dexterous manipulation, including task-scenario grounding, workspace constraint handling, and long-horizon cooperative reasoning. In addition, we propose OBiMan-Planner, a vision-language model-based zero-shot planning framework tailored for bimanual dexterous manipulation. OBiMan-Planner comprises two key components, the scenario grounding module for grounding open-world task instructions with specific scenarios and the task planning module for generating sequential stages. Extensive experiments on OBiMan-Bench demonstrate the effectiveness of our method in addressing complex bimanual dexterous manipulation tasks in open-world scenarios. The code, benchmark, and supplementary material are released at https://github.com/Zixin-Tang/OBiMan.

Dexterous Functional Grasping (DFG) is the crucial first step for humanoid robots to perform generalized manipulation tasks. However, enabling robots to learn language-guided DFG skills in real-world environments presents several challenges, including comprehending the complex relationship between task instructions and grasp functionality, generating feasible functional grasps of dexterous hands, and handling generalization for novel functional concepts. To tackle these challenges, we introduce SayFuncGrasp, a Large Language Model (LLM) based DFG framework that can synthesize versatile dexterous functional grasps from language instructions and achieve generalization on novel functional concepts. SayFuncGrasp first harnesses the open-ended manipulation knowledge from an LLM to infer grasp functionality based on language instructions. Subsequently, it employs the inferred grasp functionality to synthesize plausible DFG actions characterized by hand synergies. Simulation experiments show that SayFuncGrasp significantly outperforms the baseline method in open-set grasp functionality generalization. Real robot experiments demonstrate the effectiveness and generalizability of SayFuncGrasp for interactive humanoid manipulation tasks, achieving an overall grasp success rate of 64.66% and a manipulation success rate of 70.41%. Note to Practitioners—This research was motivated by the practical challenge of enabling humanoid robots with high-DoF dexterous hands to perform functional grasping based on verbal instructions. In industrial settings, such capabilities can significantly enhance the versatility and adaptability of humanoid assistants, allowing them to perform complex manipulations simply by being told what to do, thereby reducing programming complexity and increasing flexibility. Current dexterous functional grasping methods rely solely on visual input, without the ability to process language instructions. Furthermore, they are restricted to pre-defined functional concepts and cannot be generalized to novel object classes and manipulation tasks within natural language. Our newly proposed language-guided dexterous functional grasping system takes advantage of open-ended manipulation knowledge from LLMs to produce generalized functional grasps of dexterous robot hands according to verbal commands. Our experiment results demonstrate improved versatility and generalizability compared to the state-of-the-art.

Depth reconstruction for transparent objects is a challenging problem, where surface feature matching methods are hindered by complex refraction and reflection. Existing learning-based reconstruction methods by regressing or completing depth maps for entire scenes are data-costly and lack generalization in different environments. To solve this problem, we propose a novel transparent object reconstruction pipeline with a guided object-centric 3D diffusion model. Specifically, we train an unconditional 3D diffusion model with only 3D point cloud data. To control the output of the diffusion model, we design a silhouette-based guidance function and a completion framework with outline points for each step of diffusion process. Specifically, for each step, we design a re-projection pipeline to estimate a silhouette with uncertainty and constrain the partially-noised point cloud to align with it. We further apply stereo matching to compute the outline points in the stereo silhouettes and use a completion framework to fuse them with the partially-denoised point cloud. Finally, we transform the transparent objects to the world frame by applying the transformation from pose estimation. Experiment results show that our method can achieve state-of-the-art performance for transparent object depth reconstruction compared to existing depth regression and completion methods.

The disposal of waste electrical and electronic equipment (WEEE) presents a sustainability challenge, particularly for waste printed circuit boards (PCBs). PCBs are challenging to sort out from other waste materials in part because traditional industrial end-effectors struggle to reliably grip these irregularly shaped objects with unmodeled surface-mounted components. Vision-based separators, while effective for object categorization, face challenges with identifying precise grasp points on PCB surfaces. This paper studies regrasping control to enhance suction cup grasping performance on PCBs, addressing issues arising from uneven surfaces and intricate features that interfere with suction sealing. We categorize PCBs into two recycling levels – with large surface features intact or removed – and conduct experiments on both stationary and conveyor belt setups with realistic vision-based grasp planners. Results show that jumping regrasping improves pick-and-place success rate. Haptically driven jumping – using the Smart Suction Cup – is especially useful for unprocessed waste PCBs with large surface mount parts. The proposed method offers a promising solution to enhance the efficiency and reliability of robotic grasping in recycling applications.

Traditional strength and conditioning training relies on the utilization of free weights, such as weighted implements, to elicit external stimuli. However, this approach poses a significant challenge when attempting to modify or adjust the loads within a single training set. This paper introduces an innovative method for achieving adjustable loads during resistance training by leveraging physical Human-Robot Interaction (pHRI). The primary objective is to regulate targeted muscle activation through the use of Robo-Coach (robotic coach system). We first utilize a Task-Parameterized Gaussian Mixture Model (TP-GMM) to learn the motion of coach demonstration, which can be generalized for the trainees. The 3D path extracted from the generated trajectory is then projected onto a 2D plane with respect to the direction of the load. Furthermore, we propose a hybrid stiffness/position generator for online task execution. This generator determines the desired positions in the 2D plane according to the contact point displacements in the stimuli direction and, simultaneously, sets the desired stiffness based on the muscle activation feedback. Finally, the Robo-Coach is implemented with a variable impedance controller to achieve load-adjustable resistance training with the trainee. The biceps curl exercises were conducted and the results showed favorable performance, indicating the effectiveness of this approach.

Deformable objects especially large-size de-formable objects grasping is unappreciated but widespread in industrial applications (e.g., clothes recycling). While it encounters several challenges, for example, the existing methods didn’t take large-size deformable objects into account, no typical boundary of deformable objects. To solve the challenges, we proposed a grasp detection framework consisting of a self-trained object detection network, an instance segmentation module, and a grasp pose generation pipeline. The experiments were successfully conducted on the industrial laundry mock-up with an 88.9% success ratio. The experiments result indicates the effectiveness of the proposed framework on spatial-constrained large-size deformable objects grasping in clutter.

Learning-based methods for robotic grasping have been shown to yield high performance. However, they rely on expensive-to-acquire and well-labeled datasets. In addition, how to generalize the learned grasping ability across different scenarios is still unsolved. In this paper, we present a novel grasp adaptation strategy to transfer the learned grasping ability to new domains based on visual data using a new grasp feature representation. We present a conditional generative model for visual data transformation. By leveraging the deep feature representational capacity from the well-trained grasp synthesis model, our approach utilizes feature-level contrastive representation learning and adopts adversarial learning on output space. This way we bridge the domain gap between the new domain and the training domain while keeping consistency during the adaptation process. Based on transformed input grasp data via the generator, our trained model can generalize to new domains without any fine-tuning. The proposed method is evaluated on benchmark datasets and based on real robot experiments. The results show that our approach leads to high performance in new scenarios.

Soft object manipulation tasks in domestic scenes pose a significant challenge for existing robotic skill learning techniques due to their complex dynamics and variable shape characteristics. Since learning new manipulation skills from human demonstration is an effective way for robot applications, developing prior knowledge of the representation and dynamics of soft objects is necessary. In this regard, we propose a pretrained soft object manipulation skill learning model, namely SoftGPT, that is trained using large amounts of exploration data, consisting of a three-dimensional heterogeneous graph representation and a GPT-based dynamics model. For each downstream task, a goal-oriented policy agent is trained to predict the subsequent actions, and SoftGPT generates the consequences of these actions. Integrating these two approaches establishes a thinking process in the robot's mind that provides rollout for facilitating policy learning. Our results demonstrate that leveraging prior knowledge through this thinking process can efficiently learn various soft object manipulation skills, with the potential for direct learning from human demonstrations.

Malicious use of micro aerial vehicles (MAVs) has become a serious threat to public safety and personal privacy in recent years. Motivated by this problem, we propose a systematic approach to monitor the intrusion of malicious MAVs based on a novel type of panoramic stereo camera networks. Each sensing node of such a network consists of 16 lenses that can form a 360-degree panoramic vision system. The 16 lenses further form 8 pairs of stereo cameras that can directly localize aerial targets. The effective range for a sensing node localizing a MAV like DJI M300 could reach 80 meters, which is much farther than existing commercial stereo cameras. In terms of algorithms, we propose i) a novel visual MAV detection algorithm based primarily on motion features of MAVs, ii) an efficient stereo localization algorithm based on sparse feature points, and iii) robust multi-target tracking and trajectory fusion algorithm to fuse the observations of different sensing nodes. The effectiveness, robustness, and accuracy of the proposed algorithms together with the overall system have been verified by extensive experimental tests. To the best of our knowledge, this is the first systematic approach to detect, localize, and track unknown MAVs in the literature. Our approach provides a scalable solution to securely cover large areas of interest against malicious MAV intrusion. Note to Practitioners—Micro aerial vehicles (MAVs) have been widely used in many domains nowadays. However, they have also brought many safety problems. To monitor the intrusion of malicious MAVs, this paper proposes a novel type of panoramic stereo camera networks that can detect, localize, and track multiple MAVs simultaneously. Such a network consists of a number of sensing nodes and a central node. Each sensing node is able to detect, localize, and track multiple MAV targets. The role of the central node is to fuse the observations from multiple sensing nodes to generate more accurate trajectories of the MAV targets and in the meantime secure a large area in a coordinated way. This paper presents the details of the prototype of the system and the key algorithms therein.

This special issue of the IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING (T-ASE) focuses on how the state-of-the-art achievements and applications in the general area of artificial intelligence in automation for autonomous unmanned systems applications. As Guest Editors, we are very pleased to present the selected 16 articles, whose topics are specifically related to artificial intelligence real-time object detection, recognition, localization, control optimization, motion planning, formation control, adaptive control, and autonomous decision-making.

Object handover is one of the basic abilities for the robot to interact with the human. Most of the previous works only focus on the limited handover scenarios where the robot uses one hand to give small objects to the human. In this article, we design a bidirectional bimanual handover system that enables the robot to both give and receive the big planar object with vertical grasp posture. In addition to the basic object handover function, the designed handover system also integrates a position adjustment mechanism to improve the human experience. According to different task states, the system is divided into four modes. In each mode, the robot performs a subtask and switches to the next mode at an appropriate time. We propose a two-finger grip force controller and a dual-arm admittance NN controller to control the robot to generate actual motions. By applying specific locating, trajectory planning, and signal identifying methods, we implement the designed handover system on a Baxter robot. The system is tested on two wooden plates with different widths, thicknesses, and weights. The results show that the robot can perform the handover task safely and effectively with the designed handover system.Note to Practitioners—This article aims to solve the limitation that the robot can only hand over small objects with one hand in the human–robot handover systems. In daily life, especially in carrying tasks, many objects, such as windows, wooden boards, and big frames, may also be handed over to each other. These objects can be classified as big planar objects. To enable the robot to hand over this kind of object with the human, we design a bidirectional bimanual human–robot handover system. The designed system has three main functions. First, the robot can receive the big planar object from the human and hold the object safely with two hands, which is impossible in a single-hand handover system because the weight and size of the object are large. Then, the robot can help the human hold the object or transport it to other places. Second, the robot can adjust the object handover position according to the human’s intentions while holding the object. Because the size of objects and the height of humans may different, or some tasks require that the human position be higher or lower than the robot, the current object holding position may be hard for the current human operator to take over the object. With this function, the human can move the object to an appropriate position and then take it comfortably. Third, the robot releases the object only when it gets a clear signal. Before that, the robot always grasps the object safely, and the human can freely adjust his posture. With the designed handover system, the robot can cooperate with the human to complete more tasks indoors or in factories.

Humans in contrast to robots are excellent in performing fine manipulation tasks owing to their remarkable dexterity and sensorimotor organization. Enabling robots to acquire such capabilities, necessitates a framework that not only replicates the human behaviour but also integrates the multi-sensory information for autonomous object interaction. To address such limitations, this research proposes to augment the previously developed kernelized synergies framework with visual perception to automatically adapt to the unknown objects. The kernelized synergies, inspired from humans, retain the same reduced subspace for object grasping and manipulation. To detect object in the scene, a simplified perception pipeline is used that leverages the RANSAC algorithm with Euclidean clustering and SVM for object segmentation and recognition respectively. Further, the comparative analysis of kernelized synergies with other state of art approaches is made to confirm their flexibility and effectiveness on the robotic manipulation tasks. The experiments conducted on the robot hand confirm the robustness of modified kernelized synergies framework against the uncertainties related to the perception of environment.

A robot in the future may initially has a good learning capability but an empty library of movements. It gradually enriches its library of movements through human demonstrations. Dynamic Movement Primitives (DMPs) has been proved to be an effective way to represent trajectories. Trajectories are classified into discrete and rhythmic ones, and parameters are set for each demonstrated trajectory. However, what kind of trajectory will be provided by robot users is sometimes unknown to robot developers, so trajectory pattern and the parameters can not be determined in advance. It's also impossible for non-technical robot users to set these parameters and determine the pattern of movements they are going to demonstrate. To make it easier for non-expert robot users to programme their robots by demonstration, this work presents an efficient way to deal with these two problems. The effectiveness of the proposed methodology is proved by teaching a robot to clean the whiteboard in different ways and stack a set of cubic boxes in specific order.

Robotic hands with anthropomorphism considerations are of prominent popularity in human-centered environment. Existing anthropomorphic robotic hands achieving part or most of human hand comparable dexterity have been applied as various robotic end-effectors and prosthetics. However, two deficiencies are evident that the design for a dexterous anthropomorphic hand is largely based on the intuition of designers and the dexterity of robotic hand is hard to evaluate. To tackle these two challenges, this paper summarizes 50 hand dexterity benchmarks (HD-marks) to evaluate hand dexterity comprehensively from three perspectives. Secondly, a novel 22-DOFs soft robotic hand (S-22) replicates human hand kinematics is used to demonstrate all the 50 HD-marks. Thirdly, 7 critical joint-based kinematic motions (K-motions) and their correlation with the 50 HD-marks are established. Therefore, a clear robotic hand design guideline is built by mapping the hand functional dexterity to the required joint kinematics.

A vision-based cooperative formation control method is proposed in this paper for a heterogeneous unmanned system including an UAV (Unmanned Aerial Vehicle) and multiple UGVs (Unmanned Ground Vehicles). Considering the supervisory role of the UAV and the time-varying relative localization between UAV and UGVs, we aim at controlling the multi-UGVs to a desired formation relying only on the visual information obtained by a camera mounted on the UAV. Meanwhile, the UGV group is driven to track the flying UAV using a feedback control algorithm. A gradient descent-like control scheme which considers the visual sensing range constraint of the camera is thus adopted based on a designed cost function. Finally, the proposed method has been successfully validated through simulations.

To actively assist human walking and balance recovery, a unified active assistance control framework of the hip exoskeleton is proposed in this paper. At the beginning of this paper, the condition of active assistance is analyzed. And then, a novel virtual stiffness model is proposed based on the analysis of human hip joint torque during walking and balance recovery. The virtual stiffness model is utilized to generate the desired active assistance torque profile for active walking and balance assistance. Next, the unified active assistance control framework is established based on the virtual stiffness model. Finally, the effectiveness of the proposed control framework is demonstrated by walking experiments. The results of the forward walking experiment show that the muscle effort of iliopsoas is reduced and the amplitude of hip joint motion is enlarged with the assistance of hip exoskeleton. The walking forward pull experiments and the walking backward pull experiments show that when the human suffers from a forward or backward disturbance force during forward walking, exoskeleton can help human regain balance faster and can enlarge the margin of stability (MoS) of the human.

Industry 4.0 demands the heavy usage of robotic mobile manipulators with high autonomy and intelligence. The goal is to accomplish dexterous manipulation tasks without prior knowledge of the object status in unstructured environments. It is important for the mobile manipulator to recognize and detect the objects, determine manipulation pose, and adjust its pose in the workspace fast and accurately. In this research, we developed a stereo vision algorithm for the object pose estimation using point cloud data from multiple stereo vision systems. An improved iterative closest point algorithm method is developed for the pose estimation. With the pose input, algorithms and several criteria are studied for the robot to select and adjust its pose by maximizing its manipulability on a given manipulation task. The performance of each technical module and the complete robotic system is finally shown by the virtual robot in the simulator and real robot in experiments. This study demonstrates a setup of autonomous mobile manipulator for various flexible manufacturing and logistical scenarios.

In this paper, the position tracking control with finite-time convergence has been studied for a class of nonliear uncertain robot manipulators. Radial basis function neural network (RBFNN) based adaptive control is designed to compensate for the effect of the unknown dynamics. To achieve the finite-time convergence of both trajectory tracking error and RBFNN learning error, barrier Lyapunov functions (BLFs) and and filtering techniques are employed to design a performance function and a tracking error region to ensure position tracking error converge to a pair of specified bounds in a finite time. The effectiveness and efficiency of the proposed control method is tested and verified by simulation studies.

In this paper we present an interactive system to enhance bilateral teleoperation through online virtual fixtures generation and task switching. This is achieved using a stereo camera system which provides accurate information of the surrounding environment of the robot and of the tasks that have to be performed in it. The use of the proposed approach aims at improving the performances of bilateral teleoperation systems by reducing the human operator workload and increasing both the implementation and the execution efficiency. In fact, using our method virtual guidances do not need to be programmed a priori but they can be instead automatically generated and updated making the system suitable for unstructured environments. We strengthen the proposed method using passivity control in order to safely switch between different tasks while teleoperating under active constraints. A series of experiments emulating real industrial scenarios are used to show that the switch between multiple tasks can be passively and safely achieved and handled by the system.

In electronic manufacturing system, the design of the robotic hand with sufficient dexterity and configuration is important for the successful accomplishment of the assembly task. Due to the growing demand from high-mix manufacturing industry, it is difficult for the traditional robot to grasp a large number of assembly parts or tools having cylinder shapes with correct postures. In this research, a novel jaw like gripper with human-sized anthropomorphic features is designed for in-hand precise positioning and twisting online. It retains the simplicity feature of traditional industrial grippers and dexterity features of dexterous grippers. It can apply a constant gripping force on assembly parts and performs reliable twisting movement within limited time to meet the industrial requirements. Manipulating several cylindrical assembly parts by robot, as an experimental case in this paper, is studied to evaluate its performance. The effectiveness of proposed gripper design and mechanical analysis is proved by the simulation and experimental results.

In electronic manufacturing system, the design of the robotic hand with sufficient dexterity and configuration is important for the successful accomplishment of the assembly task. It is significant that the robot can grasp assembly parts and do some simple in-hand manipulation so as to fit them with the package slots. In this research, we study the process of precise in-hand posture transition problem using a novel jaw like gripper with human-sized anthropomorphic features. We transform the in-hand manipulation problem into a series of static grasping problems. Then we study the successful twisting condition on each grasp frame by analyzing its dynamic performance and requirements. Based on this data-driven idea, simulation and experimental data is obtained from both successful and failed trials. Finally, we create the distribution of parameters grasp map for successful twisting.

In human and robot collaborative hybrid assembly cell as we proposed, it is important to develop automatic subtask allocation strategy for human and robot in usage of their advantages. We introduce a folk-joint task model that describes the sequential and parallel features and logic restriction of human and robot collaboration appropriately. To preserve a cost-effectiveness level of task allocation, we develop a logic mathematic method to quantitatively describe this discrete-event system by considering the system tradeoff between the assembly time cost and payment cost. A genetic based revolutionary algorithm is developed for real-time and reliable subtask allocation to meet the required cost-effectiveness. This task allocation strategy is built for a human worker and collaborates with various robot co-workers to meet the small production situation in future. The performance of proposed algorithm is experimentally studied, and the cost-effectiveness is analyzed comparatively on an electronic assembly case.

In electronic manufacturing system, the design of the robotic gripper is important for the successful accomplishment of the assembly task. Due to the restriction of the architecture of traditional robotic hands, the status of assembly parts during the assembly process cannot be effectively detected. In this research, an intelligent robotic gripper - i-Hand equipped with multiple small sensors is designed and built for this purpose, getting the essential parameters for some specific mathematical model. Mating connectors by robot, as an experimental case in this paper, is studied to evaluate the performance of i-Hand. A simple new model is proposed to describe the process of mating connectors, within which the distance between the connector and deformable Printed Circuit Board (PCB) is detected by i-Hand. An online Fault Detection and Diagnosis (FDD) algorithm is proposed. Various possible situations during assembly are considered and handled according to an event driven work flow. The effectiveness of proposed model and algorithm is proved by the experiments.

An intelligent cane robot (iCane) was designed for aiding the elderly who have muscle weakness on lower limbs. A commercial omni-directional wheels robot was used as an omni-directional mobile base, and an aluminum stick was installed on the base of cane robot. A Concept called “intentional direction (ITD)” was proposed for estimating the user's walking intention by analyzing the signal of a 6-axis force/torque sensor which is fixed to the handle of stick. A universal joint driven by two DC motors was designed to control the posture of the stick. As a care-nursing device, the cane robot was designed to assist the elderly in both indoor and outdoor environments. Therefore the size and weight of cane robot should be minimized. But in that case, there is high risk that the cane robot would be pushed over by the user. In this paper a constrained nonlinear multivariable algorithm was designed to optimize the stable posture of cane robot. By controlling the posture of stick, the maximums sufferable torque moment which lead to cane robot falling over can be increased. The experimental results show that the stability of cane robot can be enhanced effectively.

Manufacturing industry tends to employ more flexible assembly cells for High-Mix, Low-Volume production. We has proposed an innovative human and robot hybrid assembly cell within this purpose to solve the problem of persistent growing cost for human resources and now and again changes in programs and configurations for robots, and to achieve a high manufacturing efficiency. One of the key issues is to find out the optimal way of allocating the assembly subtasks to both human and robot. In this paper, a model for assembly strategy generation and selection for human and robot coordinated (HRC) cell assembly is proposed. A Dual Generalized Stochastic Petri Net (GSPN) model is theoretically researched and then built based on a practical assembly task for human and robot coordination. Based on GSPN, Monte Carlo method is carried out to study the time cost and payment cost for possible strategies, and Multiple-Objective Optimization (MOOP) method related Cost-effectiveness analysis is adopted to select the optimal ones. We demonstrate the effectiveness of this approach by comparing the simulation and experimental results.