Abstract
This paper presents an immersive bilateral teleoperation framework for beyond-human-scale manipulators, combining motion/force transparency with enhanced operator embodiment through virtual reality (VR) and distributed haptic feedback. The platform integrates a full-scale industrial hydraulic manipulator, a 7-DoF haptic exoskeleton, and head-tracked visual feedback to reinforce operator agency and self-location. A force-sensorless adaptive controller incorporates an augmented human-robot dynamic model to address master-surrogate asymmetries, input nonlinearities, model uncertainties, and communication delays. Rigorous analysis establishes semi-global uniform ultimate boundedness of the closed-loop system. Extensive full-scale experiments demonstrate precise motion and force tracking with scaling up to 1:13 and 1:1000, respectively, and fixed or time-varying delays up to 150 ms. Coordinated 6-DoF pick-and-place and multi-axis contact tasks further validate system performance. A ten-participant user study supports system usability and indicates approximately 50% higher normalized embodiment scores with VR than with monitor-based feedback. These results advance intuitive, force-reflected remote manipulation at industrial scale.
Explore similar work
Jun 13, 2026cs.RO
Dexterous teleoperation requires precise arm-hand coordination, low-latency feedback, and robust interaction in real-world contact-rich environments. This paper presents a modular bilateral teleoperation framework that integrates operator-side input interfaces with a robot-side dexterous hand and compliant robotic arm in a unified control architecture. The system supports position-based hand retargeting, differential arm control, multi-scale haptic feedback, and shared control for stable manipulation. We validate the framework through a real-world dexterous manipulation task, highlighting coordinated arm-hand control and contact-aware interaction. Beyond feasibility, we identify key design insights related to cross-embodiment mismatch, haptic feedback granularity, and shared control. The proposed platform provides a practical teleoperation system and a foundation for collecting high-quality demonstrations for future learning-from-demonstration research.
Stefano Dalla Gasperina, Dong Ho Kang, Haiyun Zhang +10
Jul 1, 2026cs.RO
The four-channel teleoperation architecture is a well-established framework for achieving transparency in bilateral systems. However, its performance in human-scale teleoperation is limited by high inertia, modeling challenges, and reliance on noisy and costly force/torque sensors. This paper introduces a sensorless four-channel architecture based on inverse dynamics modeling. The controller is implemented and validated on a customized WAM bilateral teleoperation setup. Experiments demonstrate that the proposed approach outperforms conventional two- and four-channel schemes as well as transparency-enhancement methods, improving position and force tracking, reducing operator effort, and increasing maximum transmittable impedance without external sensors. A door-opening case study involving sustained whole-body contact along the manipulator further demonstrates the effectiveness of the method in realistic human-scale manipulation tasks.
Amir Noohian, Dylan Miller, Justin Valentine +2
Mar 26, 2026cs.RO
Teleoperation for contact-rich manipulation remains challenging, especially when using low-cost, motion-only interfaces that provide no haptic feedback. Virtual reality controllers enable intuitive motion control but do not allow operators to directly perceive or regulate contact forces, limiting task performance. To address this, we propose an augmented reality (AR) visualization of the impedance controller's target pose and its displacement from each robot end effector. This visualization conveys the forces generated by the controller, providing operators with intuitive, real-time feedback without expensive haptic hardware. We evaluate the design in a dual-arm manipulation study with 17 participants who repeatedly reposition a box with and without the AR visualization. Results show that AR visualization reduces completion time by 24% for force-critical lifting tasks, with no significant effect on sliding tasks where precise force control is less critical. These findings indicate that making the impedance target visible through AR is a viable approach to improve human-robot interaction for contact-rich teleoperation.
Gijs van den Brandt, Femke van Beek, Elena Torta