🚀 Abstract
Deformable Object Manipulation (DOM) remains a critical challenge in robotics due to the complexities of developing suitable model-based control strategies. Deformable Tool Manipulation (DTM) further complicates this task by introducing additional uncertainties between the robot and its environment. While humans effortlessly manipulate deformable tools using touch and experience, robotic systems struggle to maintain stability and precision. To address these challenges, we present a novel State-Adaptive Koopman LQR (SA-KLQR) control framework for real-time deformable tool manipulation, demonstrated through a case study in environmental swab sampling for food safety. This method leverages Koopman operator-based control to linearize nonlinear dynamics while adapting to state-dependent variations in tool deformation and contact forces. A tactile-based feedback system dynamically estimates and regulates the swab tool’s angle, contact pressure, and surface coverage, ensuring compliance with food safety standards. Additionally, a sensor-embedded contact pad monitors force distribution to mitigate tool pivoting and deformation, improving stability during dynamic interactions. Experimental results validate the SA-KLQR approach, demonstrating accurate contact angle estimation, robust trajectory tracking, and reliable force regulation. The proposed framework enhances precision, adaptability, and real-time control in deformable tool manipulation, bridging the gap between data-driven learning and optimal control in robotic interaction tasks
📽️ All in 2 Minutes
🔬 Overview
This project introduces State-Adaptive Koopman LQR (SA-KLQR), a novel data-driven control framework for real-time deformable tool manipulation (DTM). Our case study focuses on environmental swabbing in food safety, where maintaining precise contact force and coverage consistency is crucial.
“Precision in force control is critical for robotic swabbing to ensure effective microbial collection and surface coverage.”
✨ Key Contributions
✔ Koopman-Based Linearization → Models complex force dynamics in DTM.
✔ SA-KLQR Control Framework → Combines Koopman operators with optimal LQR control.
✔ Centroid-Based Fuzzy Regulation → Balances force distribution & minimizes tool misalignment.
✔ Robotic Swabbing Case Study → Evaluates system performance in industrial hygiene settings.
📊 Performance Evaluation
📌 Force Tracking Comparison
The table below compares SA-KLQR vs. other controllers for robotic swabbing.
| Controller | RMSE (N) | MAE (N) | Force Error (%) |
|---|---|---|---|
| 🔵 SA-KLQR | 0.006 | 0.002 | 3% ✅ |
| 🟢 PID | 0.12 | 0.08 | 10% ❌ |
| 🔴 SMC | 0.09 | 0.07 | 7% ❌ |
📊 Detailed experimental results are available in the paper.
🏆 Why SA-KLQR?
Unlike traditional controllers, SA-KLQR adapts to deformable tool dynamics, ensuring:
✅ Minimal tracking error → More precise force control.
✅ Stable tool compliance → Avoids unnecessary deformations.
✅ Higher coverage efficiency → Improves surface consistency in swabbing.
📄 Read the Paper
📄 Read Full Paper (Link to be added upon publication)
🖥️ Code & Dataset
- GitHub Repository → SA-KLQR Codebase
- Benchmark Dataset → SA-KLQR Dataset
🎥 Video Demonstration
Watch SA-KLQR in action:
📽️ Watch Here
🌍 Stay Connected
Stay updated with the latest advancements:
- GitHub → SA-KLQR Repository
- Website → SAFELAB.com
🚀 This page is continuously updated. More content like authors informations and codes coming after acceptance!