Vision-Based Hand Shadowing for Robotic Manipulation via Inverse Kinematics
This work addresses teleoperation challenges for robotics, but it is incremental as it builds on existing methods like MediaPipe Hands and inverse kinematics.
The authors tackled teleoperation of low-cost robotic manipulators by developing a vision-based hand-shadowing pipeline using inverse kinematics, achieving a 90% success rate on a structured pick-and-place benchmark but only 9.3% in unstructured real-world environments due to occlusion.
Teleoperation of low-cost robotic manipulators remains challenging due to the complexity of mapping human hand articulations to robot joint commands. We present an offline hand-shadowing and retargeting pipeline from a single egocentric RGB-D camera mounted on 3D-printed glasses. The pipeline detects 21 hand landmarks per hand using MediaPipe Hands, deprojects them into 3D via depth sensing, transforms them into the robot coordinate frame, and solves a damped-least-squares inverse kinematics problem in PyBullet to produce joint commands for the 6-DOF SO-ARM101 robot. A gripper controller maps thumb-index finger geometry to grasp aperture with a four-level fallback hierarchy. Actions are first previewed in a physics simulation before replay on the physical robot through the LeRobot framework. We evaluate the IK retargeting pipeline on a structured pick-and-place benchmark (5-tile grid, 10 grasps per tile) achieving a 90% success rate, and compare it against four vision-language-action policies (ACT, SmolVLA, pi0.5, GR00T N1.5) trained on leader-follower teleoperation data. We also test the IK pipeline in unstructured real-world environments (grocery store, pharmacy), where hand occlusion by surrounding objects reduces success to 9.3% (N=75), highlighting both the promise and current limitations of marker-free analytical retargeting.