First-person humanoid teleoperation ties access to side and rear targets to operator rotation. We present coupled visual--motor remapping, pairing a panoramic azimuth warp with its inverse upper-limb yaw transform. A working prototype implements Global Zoom (GZ) and Focus+Context (F+C). In a within-subject study (N=8); 72 trials), GZ had the largest completion count (23/24; Fixed: 20/24; F+C: 21/24), whereas F+C was preferred by 6/8 participants. Therefore, completion and preference favored different mappings in this sample.
Figures & tables
Figure 1. Coupled remapping brings side and rear robot targets into frontal operator space while matching displayed and commanded reach directions; panels show Fixed, Global Zoom, and Focus+Context.
Figure 2. Video flows robot–host–HMD; tracking and viewport state return for inverse mapping and upper-limb control.
Figure 3. Fixed preserves identity; Global Zoom uniformly compresses 360° into 180°; Focus+Context preserves more central detail with an inverse nonlinear reach mapping.
Figure 4. Participants moved bags from front ( ±30∘ ), side ( ±90∘ ), or rear ( ±120∘ ) into the central area. Completion required the entire bag inside the 40 cm outer frame; trials stopped after five knock-overs or five minutes.
Condition
Front
Side
Rear
Total
Pref.
Fixed
8/8
8/8
4/8
20/24
1/8
GZ
8/8
7/8
8/8
23/24
1/8
F+C
8/8
8/8
5/8
21/24
6/8
Table 1. Completed trials and overall-preference votes ( N=8 ).
Robot teleoperation studies how people remotely control robots through interfaces, sensing, feedback, and autonomy, despite distance, delay, and embodiment gaps.
Goal-directed eye movements are a fundamental component of visuomotor control, enabling humans to anticipate and guide their actions. Whether this anticipatory and task-driven behavior is preserved when actions are executed through a robot rather than through one's own body remains unclear. Here we address this question by investigating gaze behavior during goal-directed telemanipulation to determine how visuomotor control adapts to altered embodiment. Our findings show that gaze remains strongly aligned with task goals, preserving its predictive role even during robot-mediated manipulation. At the same time, teleoperation systematically redistributes visual attention toward the robotic end-effector and manipulated objects, increasing online monitoring. These findings show that predictive gaze is not lost under altered embodiment, but reorganized in response to changes in sensory feedback and control demands. More broadly, they reveal the flexibility of the human visuomotor system when the natural sensorimotor coupling is disrupted and identify gaze as an informative signal for inferring action intentions in human-robot interaction.
Manuela Uliano, Silvia Fattorini, Marco Controzzi
The BioRobotics Institute, Scuola Superiore Sant’Anna; Pisa, Italy · Department of Excellence in Robotics and AI, Scuola Superiore Sant’Anna; Pisa, Italy
Humanoid teleoperation for demonstration collection requires coordinated whole-body motion, continuous dexterous hand control, and viewpoint control. Existing systems either simplify hand commands or depend on dedicated wearable sensors for fine-grained hand motion. We introduce Teleopit, a full-embodiment teleoperation system that maps body, hand, and head signals from VR to a humanoid body, configurable dexterous hands, and a 2-DoF active vision module. A history encoder and failure-aware rewind sampling improve the motion tracker on both motion-capture and live VR references. An optimization-based hand retargeter combines normalized finger directions, fingertip closure, and thumb-frame alignment to map human hand motion to different dexterous hands without tuning hand-specific objective or solver hyperparameters. Component experiments evaluate tracking success rate and retargeting behavior, while real-robot teleoperation demonstrates coordinated locomotion, manipulation, and viewpoint control. ACT and GR00T N1.7 policies trained on 96 successful demonstrations collected with Teleopit achieve task success rates of 90.0% and 95.0%, respectively, when deployed on the humanoid. The project page is available at https://botrunner64.github.io/teleopit-page.
Bingqian Wu, Zicheng Xu, Xianghui Fan +2
Westlake University · Shanghai Innovation Institute
Inspired by human vision, we introduce a framework using active gaze to enable fine-grained bimanual manipulation with only a single stereo camera. EyeRobot 2.0 physically attends to a 3D fixation point in the scene by swiveling two eye viewpoints to center their gaze on it. The resulting images are processed foveally by allocating more visual tokens to the image centers, focusing computation on task-relevant features. Such Active Visual Fixation (AVF) requires carefully coordinated gaze during task execution, which we accomplish hierarchically by first training a low-level gaze servoing policy conditioned on a goal object, then training a target selector which emits fixation goals based on task progress. Both modules are trained with RL on real-world data: the first is trained with a dense geometric reward and the second co-trains with the BC gripper policy which allows it to discover fixation sequences that can resemble a human's fixation sequence while performing the task. EyeRobot 2.0 further takes advantage of fixation by canonicalizing gripper information into a fixation-relative SE(3) frame, which compacts the size of the action distribution to learn. We collect teleoperation data for 7 real-world and 6 simulated tasks, and conduct over 1000 physical and 1800 simulated robot trials comparing EyeRobot 2.0 against passive stereo and ego + wrist camera policies trained on the same data. Removing wrist cameras is costly for standard policies: with only passive stereo, real-world success drops from 52% to 27%. EyeRobot 2.0 closes this gap with only stereo, outperforming passive stereo by 40% in real and 20% in sim. It matches ego + wrist policies when their wrist views are clear (69% vs. 64%), and more than doubles their success when grasped objects occlude the wrist cameras (48% vs. 22%)