telerobotic manipulation

To address trajectory planning failures of autonomous vehicles in complex scenarios—leading to restricted operational design domains (ODDs)—this paper proposes a lightweight, loosely coupled remote assistance architecture based on arbitration graphs. Without modifying existing system code, the approach introduces a remote human intervention layer enabling real-time planning-level overrides and dynamic ODD expansion. A modular arbitration graph framework coordinates human–machine decision-making, balancing operator workload and system safety. Evaluation on two representative use cases in simulation demonstrates the architecture’s efficacy in supporting remote constraint adjustment and out-of-ODD trajectory generation, while ensuring compatibility, real-time responsiveness, and engineering deployability. The core contribution is the first “plug-and-play planning-layer” remote collaboration paradigm, establishing a novel pathway for dynamic ODD evolution.

To optimize teleoperated control efficacy of continuum soft robotic manipulators in minimally invasive surgery under remote center of motion (RCM) constraints, this paper proposes a task-priority-driven kinematic modeling and redundancy resolution framework. It presents the first systematic quantitative comparison of linear versus angular command schemes in terms of trajectory accuracy, stability, and task adaptability. The method integrates a dynamic priority allocation mechanism, a 7-degree-of-freedom (DoF) platform, and modular customized instrument interfaces. Experimental validation is conducted across multimodal physical platforms—including silicone phantoms, pegboards, and ring boards—demonstrating a 32% reduction in trajectory tracking error, a ring-transfer success rate of 96.5%, and a 41% decrease in ball-pushing path deviation. This work establishes an interpretable and scalable theoretical framework and practical paradigm for fine-grained, adaptive teleoperation of RCM-constrained soft surgical instruments.

Autonomous vehicles lack robust remote takeover support systems for safe operation on public roads. Method: This study proposes a remote takeover control center framework designed for real-road validation. Through task analysis and role-function mapping, it rigorously defines responsibility boundaries between remote operators (focused exclusively on driving接管) and fleet managers (responsible for scheduling and anomaly response). It introduces a novel, standardized state diagram covering all takeover scenarios, explicitly modeling vehicle-operator collaborative state transitions to ensure regulatory compliance and adherence to human-machine interaction prohibitions. Contribution/Results: The resulting state-machine-driven workflow and deployable architecture have enabled compliant testing and validation of multiple vehicle platforms on open roads. This work establishes a reusable, verifiable technical foundation for vehicle–road–cloud integrated remote assistance systems.

Prior research lacks empirical evidence on teleoperation over ultra-long distances (>10,000 km), particularly regarding its perceptual impact on non-expert users in elder care contexts. Method: We conducted the first systematic comparison between local and remote teleoperation across a 10,000-km distance, using a ROS-Unity integrated experimental platform and administering multi-stage standardized questionnaires to assess user perception. Contribution/Results: No statistically significant differences (p > 0.05) were found between local and remote conditions across key perceptual dimensions—including trust, sense of control, and interaction naturalness—demonstrating perceptual equivalence of ultra-long-distance remote teleoperation (RTo). This provides critical human-factors evidence and technical feasibility support for cross-regional robotic caregiving services.

To address the challenges of motion-manipulation coupling, weak environmental perception, and low intuitiveness in teleoperation of highly redundant embodied robots (e.g., CENTAURO), this paper proposes a wearable haptic human–robot interface inspired by the marionette metaphor. Our approach uniquely integrates multimodal haptic feedback—vibrotactile and force cues—deeply into a closed-loop motion mapping framework, synergizing virtual physical interaction modeling with a real-time motion–sensor co-mapping algorithm. This enables natural, full-platform kinematic mapping from operator limb motions to robot motion while concurrently rendering proprioceptive and environmental contact states. Experimental evaluation demonstrates that novice users achieve a 37% reduction in task completion time, a 52% decrease in collision misclassification rate, and a 41% reduction in NASA-TLX subjective workload—significantly enhancing operational efficiency, safety, and immersion.

This study addresses the persistent gap between theoretical control performance and its practical realization in real-world robotic systems, often caused by inadequate discretization, insufficient real-time guarantees, and weak error handling in control software. For the first time from a software engineering perspective, the authors systematically analyze 184 open-source robotic controllers through code review, empirical analysis, and test evaluation, uncovering common deficiencies in application scenarios, implementation details, and verification practices. The findings reveal that most implementations fail to properly account for critical system constraints, and their testing strategies inadequately validate the theoretical assurances they claim. This work highlights a significant disconnect between implementation quality and theoretical promises, offering concrete directions and practical guidelines for developing reliable, verifiable robotic control software.

This study addresses the absence of closed-loop benchmarks encompassing communication, visual feedback, and interruption handling in robotic hairdressing teleoperation by constructing a closed-loop teleoperation architecture evaluated on mannequin testbeds. Through multimodal network latency measurements and trajectory error analysis, this work quantifies, for the first time, the delay distribution characteristics across local, relayed, and remote deployments. Furthermore, it proposes a detection-loss rebasing algorithm to optimize interruption recovery mechanisms. The research identifies the primary sources of latency in remote mode and reveals that end-effector retraction phases dominate trajectory errors; upon excluding these segments, the root mean square error decreases to 9.6 mm. Additionally, the system’s capability for smooth motion recovery is validated, thereby establishing a systematic evaluation framework for hairdressing teleoperation.

This study addresses the challenges of high cost, operational complexity, limited fine control, and insufficient safety in teleoperating large robotic arms within construction environments. To overcome these issues, the authors propose a master-slave shared control framework that combines intuitive human guidance via a lightweight leader arm for coarse motion with autonomous, vision-based precise localization and grasping using AprilTag markers. This approach innovatively decouples human coarse-level commands from robotic fine-level execution. Experimental validation on a KUKA robotic platform demonstrates that the system substantially reduces operator workload and enhances task efficiency, while offering advantages including low cost, strong generalizability, support for demonstration data collection, and stable human-robot collaborative operation.

This study addresses the limitations of conventional joystick-based teleoperation in scenarios such as nuclear industry applications, where precise path tracking, force control, and obstacle avoidance during surface contact tasks are critical, yet impose high cognitive load on operators. To overcome these challenges, this work proposes a touchscreen-based teleoperation interface that directly maps continuous finger motion to the Franka Emika Panda robotic arm, integrating control and visualization for more intuitive motion mapping and fine-grained velocity modulation. User studies demonstrate that, compared to both joystick and one-button autonomous modes, the proposed method reduces median task completion time by 53.5% (2.50 vs. 5.38 minutes), achieves a 90.7% coverage rate on sinusoidal paths with lower overshoot, and decreases NASA-TLX cognitive workload scores by 17.3%, significantly enhancing operational efficiency and naturalness.

This study addresses the challenges of hazardous construction environments and the limited autonomy of humanoid robots by proposing a hybrid teleoperation platform based on the Unitree G1. We introduce a novel architecture integrating extended reality (XR) with foot-pedal controls, enabling a single operator to simultaneously manipulate upper-body tasks and lower-body navigation. This approach effectively resolves complex whole-body coordination challenges while ensuring high-quality data acquisition. Experimental evaluations demonstrate that the system achieves success rates of 100% and 80% in tool transportation and surface painting tasks, respectively. Although execution efficiency remains below human performance baselines, these results substantiate the platform's significant potential for deployment in dangerous construction scenarios.