State-of-the-art generalist manipulation policies have enabled the deployment of robotic manipulators in unstructured human environments. However, these frameworks struggle in cluttered environments primarily because they utilize auxiliary modules for low-level motion planning and control. Motion planning remains challenging due to the high dimensionality of the robot’s configuration space and the presence of workspace obstacles. Neural motion planners have enhanced motion planning efficiency by offering fast inference and effectively handling the inherent multi-modality of the motion planning problem. Despite such benefits, current neural motion planners often struggle to generalize to unseen, out-of-distribution planning settings. This paper reviews and analyzes the state-of-the-art neural motion planners, highlighting both their benefits and limitations. It also outlines a path toward establishing generalist neural motion planners capable of handling domain-specific challenges. For a list of the reviewed papers, please refer to https://davoodsz.github.io/planning-manip-survey.github.io/.

This paper presents a novel knowledge-informed graph neural planner (KG-Planner) to address the challenge of efficiently planning collision-free motions for robots in high-dimensional spaces, considering both static and dynamic environments involving humans. Unlike traditional motion planners that struggle with finding a balance between efficiency and optimality, the KG-Planner takes a different approach. Instead of relying solely on a neural network or imitating the motions of an oracle planner, our KG-Planner integrates explicit physical knowledge from the workspace. The integration of knowledge has two key aspects: 1) We present an approach to design a graph that can comprehensively model the workspace’s compositional structure. The designed graph explicitly incorporates critical elements such as robot joints, obstacles, and their interconnections. This representation allows us to capture the intricate relationships between these elements; 2) We train a Graph Neural Network (GNN) that excels at generating nearly optimal robot motions. In particular, the GNN employs a layer-wise propagation rule to facilitate the exchange and update of information among workspace elements based on their connections. This propagation emphasizes the influence of these elements throughout the planning process. To validate the efficacy and efficiency of our KG-Planner, we conduct extensive experiments in both static and dynamic environments. These experiments include scenarios with and without human workers. The results of our approach are compared against existing methods, showcasing the superior performance of the KG-Planner. A short video introduction of this work is available via this link. Note to Practitioners—This paper was motivated by the problem of human-robot collaboratively working on remanufacturing processes such as disassembly that require human operators and collaborative robots to work closely with each other. The robots need to plan their trajectories efficiently enough to avoid collision with humans and the trajectories need to be short enough to reduce the cycle time. Traditional motion planners usually struggle with finding a balance between efficiency and optimality, which limits wide applications of collaborative robots in remanufacturing systems that are usually less structured than manufacturing systems. This paper suggests a new planning approach that integrates the workspace’s physical information into a graph and leverages deep learning to obtain safe and near-optimal solutions quickly. Experimental studies and observations demonstrated some advantages of this approach including learning capability, efficiency, and optimality, which makes it a great potential approach to be applied to real remanufacturing processes.

Human motion prediction is a cornerstone of human-robot collaboration (HRC), as robots need to infer future movements of human workers based on observed motion cues to proactively plan their actions, ensuring safety in close collaboration scenarios. The diffusion model has demonstrated remarkable performance in predicting high-quality human motion with reasonable diversity, but suffers from a slow generative process that requires multiple times of model inference, hindering real-world applications. To enable real-time prediction, we propose training a one-step multi-layer perceptron-based (MLP-based) diffusion model using knowledge distillation and Bayesian optimization. Our method consists of two steps. First, we distill a pretrained diffusion-based motion predictor,TransFusion, directly into a one-step diffusion model with the same denoiser architecture. Then, to further reduce the inference time, we remove the computationally expensive components from the original denoiser and use knowledge distillation once again to distill the obtained one-step diffusion model into an even smaller model based solely on MLPs. Bayesian optimization is used to tune the hyperparameters for training the smaller diffusion model. To demonstrate the effectiveness of our model, we design a human-robot collaborative desktop disassembly task. The results showcase our model’s capability to forecast multiple realistic human motion in real time, addressing the uncertainty and multi-modality of human motion. Additional experimental studies are conducted on benchmark datasets to ensure fair comparisons with existing works, highlighting that our model significantly improves inference speed, achieving real-time prediction without noticeable degradation in performance. The project page is available at https://github.com/sibotian96/SwiftDiff.

This article presents a disassembly task planning algorithm considering human–robot collaboration (HRC) and human behavior prediction (HBP). Unlike assembly procedures, the disassembly of end-of-life (EOL) products has been a labor-intensive process with uncertainties difficult to cope with. Meanwhile, it is usually challenging to obtain an optimal sequence efficiently without excessive computational cost. Also, the conventional human-centered task planning, in which the robot has to halt frequently due to unsafe interruptions by human motions, may decrease the efficiency of the disassembly process. In this article, a sequence planner is proposed to assign tasks in real time between a human operator and a robot to overcome the aforementioned challenges. The cost function includes the effort of the human and the robot in terms of both movement distance and time spent on the tasks. The constraints include the disassembly rules and the safety of the human operation. The optimal sequence is generated by solving an optimization problem in a receding-horizon way. In particular, at each step, the proposed disassembly sequence planner locates the workers (a human operator and a robot) and the to-be-disassembled components, predicts human movement for the next several steps, and obtains the optimal disassembly sequence for the next several steps following disassembly rules and safety constraints. Experiments have been extensively conducted on the disassembly of a wooden toybox and a used hard disk drive (HDD) to validate the proposed disassembly sequence planner. The planner has successfully generated the disassembly sequence in an HRC setting explicitly considering real-time human motion prediction and assigned the human operator and the robot to collaboratively complete disassembly tasks without violating disassembly rules and safety constraints.

Under the premise of ensuring the security and integrity of the blockchain, the editable blockchain realizes the correction operation of the data on the chain, but the number of edits is not limited, so that malicious users can modify the data on the chain infinitely in the case of obtaining a trapdoor. Based on this, a function construction scheme that combines the Elliptic Curve Discrete Logarithm Problem difficulty assumption with a cryptographic primitive called one-time chameleon hash function is proposed.A controllable variable is introduced for the hash generation part of the chameleon hash function, so that the same hash value can find two preimages without revealing trapdoor information, However, when searching for the third pre-image, the secondary use of the set controllable variable results in "access failure", i.e., satisfying the secondary collision resistance, The security of the scheme under the assumption of Elliptic Curve Discrete Logarithm Problem is proved in the random oracle model, and a chameleon hash algorithm that is also resistant to secondary editing is selected for experimental comparative analysis. The results show that the scheme in this paper reduces the number of modal operations, which makes the algorithm complexity reduced. It is able to improve the computing efficiency of hash generation algorithm and hash collision algorithm significantly with the same security strength. The combination of the amendment privilege restriction of the chameleon hash function and the efficiency of its algorithm is realized, which provides a technical reference for the amendment privilege management of editable blockchain.

There is an increasing popularity in exploiting modern vehicles as mobile sensors to obtain important road information such as potholes, black ice and road profile. Availability of such information has been identified as a key enabler for next-generation vehicles with enhanced safety, efficiency, and comfort. However, existing road information discovery approaches have been predominately performed in a single-vehicle setting, which is inevitably susceptible to vehicle model uncertainty and measurement errors. To overcome these limitations, this paper presents a novel cloud-assisted collaborative estimation framework that can utilize multiple heterogeneous vehicles to iteratively enhance estimation performance. Specifically, each vehicle combines its onboard measurements with a cloud-based Gaussian process (GP), crowdsourced from prior participating vehicles as “pseudo-measurements”, into a local estimator to refine the estimation. The resultant local onboard estimation is then sent back to the cloud to update the GP, where we utilize a noisy input GP (NIGP) method to explicitly handle uncertain GPS measurements. We employ the proposed framework to the application of collaborative road profile estimation. Promising results on extensive simulations and hardware-in-the-loop experiments show that the proposed collaborative estimation can significantly enhance estimation and iteratively improve the performance from vehicle to vehicle, despite vehicle heterogeneity, model uncertainty, and measurement noises.

State-of-the-art exoskeletons are typically limited by low control bandwidth and small range stiffness of actuators which are based on high gear ratios and elastic components (e.g., series elastic actuators). Furthermore, most exoskeletons are based on discrete gait phase detection and/or discrete stiffness control resulting in discontinuous torque profiles. To fill these two gaps, we developed a portable lightweight knee exoskeleton using quasi-direct drive (QDD) actuation that provides 14 Nm torque (36.8% biological joint moment for overground walking). This paper presents 1) stiffness modeling of torque-controlled QDD exoskeletons and 2) stiffness-based continuous torque controller that estimates knee joint moment in real-time. Experimental tests found the exoskeleton had high bandwidth of stiffness control (16 Hz under 100 Nm/rad) and high torque tracking accuracy with 0.34 Nm Root Mean Square (RMS) error (6.22%) across 0-350 Nm/rad large range stiffness. The continuous controller was able to estimate knee moments accurately and smoothly for three walking speeds and their transitions. Experimental results with 8 able-bodied subjects demonstrated that our exoskeleton was able to reduce the muscle activities of all 8 measured knee and ankle muscles by 8.60%-15.22% relative to unpowered condition, and two knee flexors and one ankle plantar flexor by 1.92%-10.24% relative to baseline (no exoskeleton) condition.

The evaluation of driver's steering comfort, which is mainly concerned with the haptic driver-vehicle interaction, is important for the optimization of advanced driver assistance systems. The current approaches to investigating steering comfort are mainly based on the driver's subjective evaluation, which is time-consuming, expensive, and easily influenced by individual variations. This paper makes some tentative investigation of objective evaluation, which is based on the electromyogram (EMG) and movement trajectory of the driver's upper limbs during steering maneuvers. First, a steering experiment with 21 subjects is conducted, and EMG and movement trajectories of the driver's upper limbs are measured, together with their subjective evaluation of steering comfort. Second, five evaluation indices including EMG and movement information are defined based on the measurements from the first step. Correlation analyses are conducted between each evaluation index and steering comfort rating (SCR), and the results show that all of the indices have significant correlations with SCR. Then, an artificial neural network model is devised based on the aforementioned indices and its predicting performance of SCR is demonstrated as acceptable. The results reveal that it may be feasible to establish an objective evaluation approach for vehicle steering comfort.

Most industrial robots are actuated using geared motors with no direct load side measurement. The flexibility introduced by the gear reducer causes transmission errors and vibrations, which limits the adoption of robot manipulators in many demanding applications. This paper presents a lean and efficient scheme of iterative learning control (ILC) to compensate for the joint flexibility of industrial robot manipulators. A two-degree-of-freedom ILC method is introduced. Compared with the dual-stage ILC that has been previously proposed for servo flexibility compensation, the method is more effective and also enables a leaner implementation. In addition, in order to handle system variation, a robust synthesis method is developed by using H∞ and μ techniques in an innovative way. The proposed method is analyzed using simulation studies as well as tested on an actual industrial robot manipulator.

The new development of embedded systems has the potential to revolutionize our lives and will have a significant impact on future Internet of Thing (IoT) systems if required services can be automatically discovered and accessed at runtime in Machine-to-Machine (M2M) communication networks. It is a crucial task for devices to perform timely service discovery in a dynamic environment of IoTs. In this article, we propose a Socioecological Service Discovery (SESD) model for advanced service discovery in M2M communication networks. In the SESD network, each device can perform advanced service search to dynamically resolve complex enquires and autonomously support and co-operate with each other to quickly discover and self-configure any services available in M2M communication networks to deliver a real-time capability. The proposed model has been systematically evaluated and simulated in a dynamic M2M environment. The experiment results show that SESD can self-adapt and self-organize themselves in real time to generate higher flexibility and adaptability and achieve a better performance than the existing methods in terms of the number of discovered service and a better efficiency in terms of the number of discovered services per message.

Summary Successful and efficient key management plays a critical role toward secured computing and communication systems. Many authenticated key agreement protocols have been proposed to meet the great challenges of information security. The two‐party key agreement protocol can be classified into two categories: implicit and explicit key authentications. This paper investigates the issue on authenticated two‐party key agreement protocol over an insecure public network and extends the existing implicit authenticated key agreement protocol into the explicit one by introducing the authenticators. The advantage of this explicit protocol is that it does not need any fixed public key infrastructure. Furthermore, this explicit protocol is provably secure in the random oracle under the Computation Gap Diffie‐Hellman assumption. Concurrency and Computation: Practice and Experience, 2013.© 2013 Wiley Periodicals, Inc.

Wireless mesh networks are widely applied in many fields such as industrial controlling, environmental monitoring, and military operations. Network coding is promising technology that can improve the performance of wireless mesh networks. In particular, network coding is suitable for wireless mesh networks as the fixed backbone of wireless mesh is usually unlimited energy. However, coding collision is a severe problem affecting network performance. To avoid this, routing should be effectively designed with an optimum combination of coding opportunity and coding validity. In this paper, we propose a Connected Dominating Set (CDS)-based and Flow-oriented Coding-aware Routing (CFCR) mechanism to actively increase potential coding opportunities. Our work provides two major contributions. First, it effectively deals with the coding collision problem of flows by introducing the information conformation process, which effectively decreases the failure rate of decoding. Secondly, our routing process considers the benefit of CDS and flow coding simultaneously. Through formalized analysis of the routing parameters, CFCR can choose optimized routing with reliable transmission and small cost. Our evaluation shows CFCR has a lower packet loss ratio and higher throughput than existing methods, such as Adaptive Control of Packet Overhead in XOR Network Coding (ACPO), or Distributed Coding-Aware Routing (DCAR).

With the development and maturity of cloud computing technology, the demand for cloud storage is growing. Deduplication is a basic requirement for cloud storage to save storage space of cloud servers. And as clients are untrusted from the perspective of the server, the notion of Proofs of Ownership (PoWs) has been proposed in client-side deduplication. On the other hand, the clients cannot completely trust the server either, thus clients have to know whether their files are stored integrally in the cloud. However, most existing works only focus on one-way validation. In this paper, we introduce a framework called Proofs of Ownership and Retrievability (PoOR) considering the requirement of mutual validation. In our PoOR scheme, clients can prove to the server their ownership of files and verify the retrievability of the files without uploading or downloading them. For ensuring the recoverability and security of files in server, we encode files by erasure code. In order to keep the communication cost in constant, we employ Merkle Tree and homomorphic verifiable tags which also induce acceptable storage overheads. At last, we implemente our scheme and compare it with other schemes. The result shows that the PoOR scheme is efficient in computation performance, especially when the size of the file is large.

Yao first introduced a constant-round protocol for secure two-party computation (2PC) withstanding semi-honest adversaries by using a tool called "garbled circuit". Later, many protocols based on garbled circuit approach have been presented, most of which discussed malicious adversaries and efficiency about 2PC. However, there only have a few protocols dealing with the fundamental property of fairness for Yao's garbled circuit approach, in which one involved a trusted third party and the others are very expensive. In the paper, we propose' an efficient Yao's garbled circuit protocol for fair secure 2PC based on ElGamal encryption, Pedersen commitment, Cachin et al.'s verifiable oblivious transfer and Ou-Ruan et al.'s gradual release homomorphic timed commitment. The protocol achieves two advantages: it doesn't need the third party and it is more efficient than other fair secure Yao's protocols.

This paper considers the issue on authenticated two-party key agreement protocol over an insecure public network. Many authenticated key agreement protocols have been proposed to meet the challenges. Based on bilinear pairing, Zhou et al. proposed a two-party key agreement with implicit key authentication in 2011. Implicit key authentication can be easily achieved by encrypting the later communications using the session key. However, we can not ensure how participants using the session key protocol. This paper will transform the Zhou's implicit authenticated key agreement protocol into the explicit one with key confirmation by introducing the authenticators, and show the proposed protocol is provably secure under the random-oracle model.

Due to the wide application fields, many people research Wireless Sensor Networks(WSNs). Currently, more and more researchers focus on the existence of selfish nodes which do not cooperate in routing and forwarding. Traditional methods need too much resource or do not adapt to menace from inner of network. In this paper, we propose a reliable routing model against selfish nodes. Game theory is used in our model to find the balance between the reliability and resource limitation. In simulation, we introduce our model into DSR routing as DSR-G. The simulation result shows the selfish nodes has less infections in DSR-G than DSR.