Adaptive Anomaly Recovery for Telemanipulation: A Diffusion Model Approach to Vision-Based Tracking

2025-03-11

AI Summary

To address visual tracking instability in teleoperation caused by occlusions, low illumination, and other anomalies, this paper proposes the Diffusion-Enhanced Teleoperation (DET) framework. DET pioneers the integration of diffusion models into real-time visual tracking recovery, combining a lightweight frame-difference detection (FDD) mechanism to precisely localize anomalous video segments and directly reconstructing missing frames in the high-dimensional pixel space. This end-to-end approach preserves the original video’s spatiotemporal structure, avoiding information loss inherent in low-dimensional interpolation methods. Experiments across diverse occlusion scenarios demonstrate that DET reduces average RMSE by 17.2% and 51.1% compared to cubic spline and FFT interpolation, respectively. The framework significantly enhances tracking robustness and teleoperated control stability, establishing a new paradigm for anomaly-resilient visual tracking in real-time robotic teleoperation systems.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Dexterous telemanipulation critically relies on the continuous and stable tracking of the human operator's commands to ensure robust operation. Vison-based tracking methods are widely used but have low stability due to anomalies such as occlusions, inadequate lighting, and loss of sight. Traditional filtering, regression, and interpolation methods are commonly used to compensate for explicit information such as angles and positions. These approaches are restricted to low-dimensional data and often result in information loss compared to the original high-dimensional image and video data. Recent advances in diffusion-based approaches, which can operate on high-dimensional data, have achieved remarkable success in video reconstruction and generation. However, these methods have not been fully explored in continuous control tasks in robotics. This work introduces the Diffusion-Enhanced Telemanipulation (DET) framework, which incorporates the Frame-Difference Detection (FDD) technique to identify and segment anomalies in video streams. These anomalous clips are replaced after reconstruction using diffusion models, ensuring robust telemanipulation performance under challenging visual conditions. We validated this approach in various anomaly scenarios and compared it with the baseline methods. Experiments show that DET achieves an average RMSE reduction of 17.2% compared to the cubic spline and 51.1% compared to FFT-based interpolation for different occlusion durations.

Problem

• Improves stability in vision-based telemanipulation tracking.
• Addresses anomalies like occlusions and lighting issues.
• Uses diffusion models for robust video reconstruction.

Innovation

• Uses diffusion models for anomaly recovery
• Incorporates Frame-Difference Detection technique
• Reconstructs video clips with diffusion models