Uploaded April 2026 | Updated September 2026, 3 weeks ago
The robotics teams at Analog Devices (ADI) collaborated with MANUS, leveraging the data gloves to advance dexterous robotic manipulation through human‑in‑the‑loop, simulation‑based training.
Serving as the human motion capture layer in ADI's simulation-based training pipeline, the MANUS glove enables an operator to teleoperate a simulated dexterous robotic hand; the IPC physics engine then generates real-time tactile sensor data and deformable contact feedback. The joint effort and paired dataset of human motion and simulated tactile response highlight how combining human expertise with physically grounded hardware simulation can help close the sim‑to‑real gap for robotic manipulation.
The robotics teams at Analog Devices (ADI) collaborated with MANUS, leveraging the data gloves to advance dexterous robotic manipulation through human‑in‑the‑loop, simulation‑based training.
Serving as the human motion capture layer in ADI's simulation-based training pipeline, the MANUS glove enables an operator to teleoperate a simulated dexterous robotic hand; the IPC physics engine then generates real-time tactile sensor data and deformable contact feedback. The joint effort and paired dataset of human motion and simulated tactile response highlight how combining human expertise with physically grounded hardware simulation can help close the sim‑to‑real gap for robotic manipulation.
