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Robotic automation has significantly improved efficiency and precision in repetitive tasks performed in structured environments such as manufacturing. However, many real-world tasks remain extremely challenging to fully automate. Robots still struggle to operate reliably in dynamic, uncertain, and unstructured environments, where perception, decision-making, and physical interaction are complex.
In many high-risk domains, such as surgery, disaster response, nuclear facility maintenance, and space exploration, complete autonomy is not yet feasible due to the extremely high requirements for safety, reliability, and robustness. As a result, human-in-the-loop robotic systems remain essential for these applications.
Teleoperation enables human operators to remotely control robotic systems in such environments. However, remote operation often suffers from limited sensory feedback, communication delays, and reduced situational awareness. These limitations can make complex manipulation tasks difficult for human operators to perform reliably.
Our research focuses on collaborative teleoperation and shared autonomy, where control authority is intelligently shared between the human operator and the robotic system. By combining human intuition and decision-making with robotic precision and autonomy, we aim to develop teleoperation systems that enable safer, more efficient, and more reliable task execution in challenging environments.
These technologies have strong potential in applications such as robot-assisted surgery, disaster and rescue operations, nuclear power plant maintenance, and space robotics.
Key Research Topics
Key Research Topics
1️⃣ Shared Teleoperation Control Architecture
1️⃣ Shared Teleoperation Control Architecture
Design of shared-control frameworks that dynamically allocate control authority between human operators and autonomous robotic systems, including authority management and seamless human–robot collaboration.
2️⃣ Collaborative Assistance for Precision Manipulation
2️⃣ Collaborative Assistance for Precision Manipulation
Collaborative pose correction and robotic assistance for teleoperated precision tasks such as insertion and manipulation, including adaptive motion scaling and task-dependent assistance.
3️⃣ Operator Intention Estimation and Prediction
3️⃣ Operator Intention Estimation and Prediction
Learning-based methods to infer operator intent from partial motion and interaction patterns, enabling goal inference, surgeon-intention-driven motion scaling, and improved teleoperation performance.
4️⃣ Semi-Autonomous Teleoperation for Safety-Critical Tasks
4️⃣ Semi-Autonomous Teleoperation for Safety-Critical Tasks
Semi-autonomous control strategies that assist human operators during complex operations, including smooth blending between manual and autonomous control, context-aware task adaptation, and applications such as robotic super-microsurgery.
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