Domain-Adaptive Communication-Rate Optimization for Sim-to-Real Humanoid-Robot Wireless XR Teleoperation
- URL: http://arxiv.org/abs/2605.19293v1
- Date: Tue, 19 May 2026 03:13:09 GMT
- Title: Domain-Adaptive Communication-Rate Optimization for Sim-to-Real Humanoid-Robot Wireless XR Teleoperation
- Abstract summary: Wireless extended reality (XR) teleoperation provides embodied interaction capability for collecting humanoid robot demonstrations.<n>This paper develops a system framework that integrates sampling, transmission, the reconstruction and formulates a communication-rate optimization.
- Score: 45.11184664928145
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Wireless extended reality (XR) teleoperation provides embodied interaction capability for collecting humanoid robot demonstrations, but the large-scale adoption is restricted by the overhead of high-frequency motion transmission. This paper develops a system framework that integrates sampling, transmission, interpolation, and reconstruction and formulates a communication-rate optimization that aims to minimize the communication energy while maintaining the reconstruction accuracy of robot motion trajectories through dimension-wise sampling-rate control. Since acquiring real-time feedback from physical robots is limited by hardware costs, it is necessary to solve the problem through simulator interaction with offline real-domain data correction. To guide sim-to-real adaptation, we provide a PAC-Bayes generalization characterization that reveals the effects of latent density-ratio estimation, finite-sample deviation, and encoder bias. Building on this analysis, we propose a proximal policy optimization (PPO) method with density-ratio weighting and trust-region regularization. Experiments on public humanoid teleoperation dataset show that the proposed method improves the tradeoff between reconstruction error and communication energy consumption under sim-to-real distribution shift. We further analyze the effectiveness of the proposed algorithm across various wireless channels and dynamic motion trajectories.
Related papers
- A Neural Network Based Teleoperation for Remote Controlled Vehicles [98.38733381483934]
Direct teleoperation of vehicles faces critical technical bottlenecks.<n> communication latency and the operator's inability to physically perceive unmodeled environmental disturbances.<n>We propose a tailored unilateral teleoperation framework.
arXiv Detail & Related papers (2026-08-11T01:51:08Z) - Decentralized Pose Graph Riemannian Optimization for Object-based Multi-Robot SLAM [14.190643126006819]
In object-based multi-robot SLAM, the problem becomes more tightly coupled because robots must jointly estimate both their trajectories and the poses of persistent objects observed by multiple agents.<n>Existing decentralized solutions often assume that the communication graph closely matches the physical interaction topology, which is restrictive in realistic deployments where communication is sparse, intermittent, or time-varying.<n>This paper presents a fully decentralized Riemannian optimization framework for object-based multi-robot PGO that decouples the coupled estimation problem via a consensus mechanism, enabling flexible communication topologies.
arXiv Detail & Related papers (2026-06-23T12:21:34Z) - X-OP: Cross-Morphology Whole-Body Teleoperation via MPC Retargeting [3.134050607080983]
Whole-body teleoperation is essential for scalable robot data collection in loco-manipulation tasks.<n>We propose a hierarchical whole-body teleoperation framework driven by a single XR device.<n>We show higher success rates on whole-body control tasks for both a humanoid and a mobile manipulator.
arXiv Detail & Related papers (2026-06-06T01:50:59Z) - World Model-Enabled Causal Digital Twins for Semantic Communications in Physical AI Systems [37.02293452905313]
This paper investigates goal-oriented semantic communications for physical AI systems with closed-loop sensing-communication-inference-control.<n>In particular, the problem of semantic communications is formulated as a long-term return-per-bit under wireless bit-budget constraints.<n>A world-model-enabled causal digital twin (WM-CDT) framework is proposed to capture the dynamics of closed-loop AI systems.
arXiv Detail & Related papers (2026-05-15T18:41:51Z) - PMG: Parameterized Motion Generator for Human-like Locomotion Control [14.637220434597168]
We develop a real-time motion generator that produces human-like locomotion in a single integrated system.<n>We show that, within a single integrated system, PMG produces natural, human-like locomotion, responds precisely to high-dimensional control inputs.<n>These results establish a practical, experimentally validated pathway toward natural and deployable humanoid control.
arXiv Detail & Related papers (2026-02-13T06:38:04Z) - Adaptive Pareto-Optimal Token Merging for Edge Transformer Models in Semantic Communication [27.78647101651565]
Large-scale transformer models have emerged as a powerful tool for semantic communication systems.<n>We present a training-free framework for adaptive token merging in pretrained vision transformers to jointly reduce inference time and transmission resource usage.
arXiv Detail & Related papers (2025-09-11T06:05:35Z) - Backscatter Device-aided Integrated Sensing and Communication: A Pareto Optimization Framework [59.30060797118097]
Integrated sensing and communication (ISAC) systems potentially encounter significant performance degradation in densely obstructed urban non-line-of-sight scenarios.<n>This paper proposes a backscatter approximation (BD)-assisted ISAC system, which leverages passive BDs naturally distributed in environments of enhancement.
arXiv Detail & Related papers (2025-07-12T17:11:06Z) - Deep Learning Optimization of Two-State Pinching Antennas Systems [48.70043547158868]
Pinching antennas (PAs) can dynamically control electromagnetic wave propagation through binary activation states.<n>In this work, we investigate the problem of optimally selecting a subset of fixed-position PAs to activate in a waveguide, when the aim is to maximize the communication rate at a user terminal.
arXiv Detail & Related papers (2025-07-08T17:55:54Z) - Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning [59.660724802286865]
Unmanned aerial vehicles (UAVs) have emerged as the potential aerial base stations (BSs) to improve terrestrial communications.<n>This work employs collaborative beamforming through a UAV-enabled virtual antenna array to improve transmission performance from the UAV to terrestrial mobile users.
arXiv Detail & Related papers (2025-02-09T09:15:47Z) - Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks [55.467288506826755]
Federated learning (FL) has been recognized as a viable solution for local-privacy-aware collaborative model training in wireless edge networks.<n>Most existing communication-efficient FL algorithms fail to reduce the significant inter-device variance.<n>We propose a novel communication-efficient FL algorithm, named FedQVR, which relies on a sophisticated variance-reduced scheme.
arXiv Detail & Related papers (2025-01-20T04:26:21Z) - Rethinking the Tradeoff in Integrated Sensing and Communication:
Recognition Accuracy versus Communication Rate [21.149708253108788]
Integrated sensing and communication (ISAC) is a promising technology to improve the band-utilization efficiency.
There exists a tradeoff between the sensing and communication performance.
This paper formulates and solves a multi-objective optimization problem which simultaneously maximizes the recognition accuracy and the communication data rate.
arXiv Detail & Related papers (2021-07-20T17:00:35Z) - Path Design and Resource Management for NOMA enhanced Indoor Intelligent
Robots [58.980293789967575]
A communication enabled indoor intelligent robots (IRs) service framework is proposed.
Lego modeling method is proposed, which can deterministically describe the indoor layout and channel state.
The investigated radio map is invoked as a virtual environment to train the reinforcement learning agent.
arXiv Detail & Related papers (2020-11-23T21:45:01Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.