Organizations: 1Symbiotic and Augmented Intelligence Lab, Georgia Institute of Technology, Atlanta, GA, USA. · 2Robot Danger Lab, Georgia Institute of Technology, Atlanta, GA, USA.
Dual-operator humanoid teleoperation enables collaborative manipulation through two separate robots, but operators lack direct awareness of robot-object contact that is normally available during physical interaction. To address this, we present eXtended Reality Teleoperation (XRT), an extended reality teleoperation framework that returns a robot's fingertip contact through lightweight vibrotactile and/or auditory feedback, and a synchronized dual-humanoid testbed built on it for two operators. Sixteen dyads performed a collaborative crate lift-and-flip under vision only and the three feedback conditions in a counterbalanced within-subject design with verified feedback-channel delivery. Three results follow. First, operators rated our finger-localized vibrotactile display as more effective than our open-ear sonification: all 32 ranked it above the sonification, 18 ranked it most effective, and its cues were rated far easier to perceive and interpret than the auditory cues. Second, completion time, failures, workload, discomfort, and partner awareness showed no detected condition differences, with wide completion-time intervals and smaller meaningful effects unresolved. Third, performance changed substantially with practice, especially on the harder flip-over task: completion time improved across blocks, and attempt-to-attempt variation within a dyad (SD x1.51) was larger than the largest condition contrast (x1.16). Exploratory analyses detected no difference in immediate grip or motion responses between feedback conditions. For novice collaborative teleoperation, our fingertip vibrotactile cues were rated more effective and easier to interpret than our auditory cues, while their effect on task performance remains unresolved.
Figures & tables
Fig. 1: XRT signal paths for one station; the other mirrors them. Tracked pose commands the robot; camera video (V) and hand-contact cues (A/T) return to the operator. Robots kneel with fixed torsos for this tabletop task, without locomotion or balancing; only arms, hands, and neck are commanded. Photo staged with the study curtain drawn back; cue sketches are schematic.
Fig. 2: The dual-humanoid testbed built on XRT, shown for the master station; the peer mirrors its hardware and signal paths. Both robots manipulate the shared tagged crate. Black arrows carry commands; red arrows carry sensing and feedback. The two-way study link carries master-to-peer lock-step commands, return tracking/status traffic, and clock synchronization (the 46 ms shown is the maximum logged offset; median 2.1 ms). Shared tracking is shown once; synchronized per-take logging is described in Sec. III-E.
Light
Medium
High
Maximum
Activation band
0.10–0.35
0.35–0.60
0.60–0.85
0.85–1
Glove amplitude (of 100)
10
25
50
70
Glove pulses (20 ms)
1
1
1
2
Bell octave (upward)
0
+1
+1
+2
Bell loudness (relative)
0.10
0.35
0.60
0.85
TABLE I: Shared mapping of normalized tactile-skin activation (Sec. III-D); downward band changes play the reversed bell an octave lower.
Fig. 3: Perceived effectiveness and interpretability (32 operators, 16 dyads). (a) Ranks from most (1) to least (4) effective for completing the task, with the number of operators ranking each condition most effective. (b) Perception-item means for our auditory (red) and vibrotactile (blue) cues; 95% CIs resample complete dyads. The eight-item mean and its paired effect size dz summarize the display-specific advantage.
Fig. 4: No detected objective condition effect. (a) Time to flip complete (secondary outcome) by block and condition. Open triangles are the eight failures assigned 180 s; squares on the solid line and circles on the dashed line are the per-block medians of finished flip-over and flip-back attempts. (b) Three pre-specified primary completion-time contrasts: mixed-model ratios and 95% CIs, with the numerator condition listed first. The dashed line is equal time; Holm p = .71 for all three.
Robot teleoperation studies how people remotely control robots through interfaces, sensing, feedback, and autonomy, despite distance, delay, and embodiment gaps.
Enabling operators to perceive and interact with remote environments naturally is a fundamental challenge in robotic teleoperation. This is especially critical in tasks involving physical interaction, where real-time haptic awareness improves operational safety and effectiveness. Existing kinesthetic haptic feedback methods suffer from instability during rigid surface contacts and remain sensitive to communication delays, while visual cue-based force feedback imposes additional cognitive load and limits sustained situational awareness. This work presents a teleoperation interface that conveys remote surface interactions to the operator through electrovibration-based tactile feedback, enabling naturally mapped force reflection while avoiding the stability issues associated with kinesthetic feedback and the latency limitations of mechanical actuators. A user study (N=21) evaluated interface usability, sense of presence, and operator workload under two force reflection conditions: visual feedback and electrovibration-based tactile feedback. Characterisation experiments further assessed path-following accuracy and response time across both conditions. Results show that tactile feedback significantly reduced response time by 15.35% (p=0.002, d=0.96) and increased the sense of presence by 31% (p<0.001, d=0.90) compared to visual feedback, while imposing comparable workload and usability across both conditions. These findings demonstrate that electrovibration-based tactile feedback is a viable and effective modality for robot teleoperation, improving operator responsiveness and sense of presence in contact-rich manipulation tasks, with direct applicability to safety-critical domains such as nuclear maintenance.
Alperen Kenan, Juan José García Cárdenas, Adriana Tapus +2
Bristol Robotics Laboratory, University of the West of England, Bristol, United Kingdom · Computer Science and System Engineering Department (U2IS), Autonomous Systems and Robotics Lab, ENSTA, Institut Polytechnique de Paris, Paris, France · Kempten University of Applied Sciences, Kempten, Germany
A fundamental challenge in robotic teleoperation is enabling an operator to control a remote robot as effortlessly and intuitively as their own hands. Despite the growing use of teleoperation to collect demonstration data for training autonomous robot policies, teleoperated robot performance still falls significantly short of human dexterity, even for basic tasks. Here, we present evidence that a key factor contributing to this performance gap is the absence of spatially distributed tactile feedback. Using a two-degree-of-freedom (DoF) bilateral force-feedback telemanipulator paired with a 32-DoF tactile fingertip display, we show that operator performance improves significantly when localized deformations on the remote manipulator are faithfully reproduced on the operator's fingertip. In a series of teleoperation tasks, reproducing distributed contact information not only accelerated task performance but also brought teleoperated movements closer to natural human behavior by minimizing corrective actions and task completion steps, thereby reducing the deviation between teleoperated and natural trajectories by 29\unicodex201379%. Furthermore, we found that increasing the resolution of the tactile feedback\unicodex2014by refining how finely the measured displacements were quantized for reproduction\unicodex2014compressed the state-space distribution of teleoperated motions, which has been associated with improved training outcomes for autonomous robot policies. Together, these results suggest that spatially distributed tactile feedback is essential for closing the gap between human and teleoperated dexterity and training the next generation of autonomous robots.
Rohan Kota, Gregory Reardon, J. Edward Colgate
Center for Robotics and Biosystems, Northwestern University, Evanston, IL.
In robot teleoperation, haptic feedback can be used to help human operators accomplish dexterous manipulation tasks. However, existing haptic feedback methods try to replicate high-fidelity sensory haptics that are felt in real world interactions, which are constrained by the sensing and feedback hardware capability and may lead to higher workload. To addresses these limitations, this work introduces semantic haptics for teleoperation, which uses abstract haptic patterns to convey critical information about robot states. We categorize robot states into "Confirmations" and "Exceptions", implement a modular haptic rendering pipeline in robot simulation, and deliver semantic haptic feedback to operators through pneumatic and vibrotactile wristbands. This simplifies hardware requirements and enables one-to-many mappings between haptic patterns and robot states. Through three evaluation studies, we identify the most effective semantic haptic design for a common pick and place teleoperation task and compare semantic haptics to other teleoperation feedback approaches including sensory haptics and visual feedback. Results suggest that while semantic haptics performs similarly as other feedback in unimanual tasks, it achieves superior performance in bimanual tasks, with reduced task workload, increased situational awareness, and overall preference.