Dexterous
Manipulation
Dexterous manipulation is the precise and flexible control of robotic hands, enabling human-like object handling. It requires coordinated movements, force control, and adaptive grasping, supported by sensors, control algorithms, and AI for efficient manipulation in robotics and automation.
Dexterous Manipulation leveraging Vision-Language Model
Planning dexterous hand manipulation action using Vision-Language Model (VLM). Based on user’s request, VLM infers the most suitable object within the scene, and plans the corresponding hand posture. By leveraging VLM’s pre-trained knowledge, we can conduct various hand postures including tool manipulation action.
Transfer Human Motion to Physically Feasible Robotic Actions Using Reinforcement Learning
Refine Human Object Interaction Data to Physical Feasible Robotic Action using Reinforcement Learning. By using our method, we can effectively reduce embodiment gap between human and robot.
Imitation Learning & Teleoperation System for Humanoid Bimanual Manipulation
Imitation Learning
Teleoperation
Teleoperating a humanoid and learning through imitation. Using VR, we teleoperate the humanoid’s upper body to collect data. Through imitation learning, the humanoid can perform various tasks.
Robot Hand & Arm for Unseen Objects Grasping
Vision based Manipulation + Brain in Hand System Intergration
Traditional vision-based manipulator systems have limitations such as a narrow field of view or the potential to obscure objects due to the fixed position of the camera. Our laboratory is researching a vision-based intelligent robot hand that not only recognizes objects but also includes perception and control. This is achieved by building an interface on the robot hand that incorporates sensors and controllers.
Robotic Palm
Soft Robotic Palm with Tunable Stiffness Using Dual-Layered Particle Jamming Mechanism
This project presents a novel robotic palm with a dual-layered structure designed to yield high surface conformity and controllable rigidity for enhanced grasping performance. It comprises a vacuum chamber for adjusting the stiffness of the palm via particle jamming and an air chamber for actively controlling the palm deformation. An auto-jamming control scheme that automatically solidifies the palm by sensing the internal pressure of the palm without any tactile sensors or visual feedback was also proposed.
Alternative grasping strategy
Pick-and-place scenario