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About

Jieren Deng is a rising researcher at the intersection of robotics, computer vision, and human-robot interaction, whose work focuses on enabling robots to perceive, predict, and adapt to dynamic environments. Deng’s key research areas include human motion forecasting for assistive exoskeletons, incremental object detection for robotic manipulation, and 3D instance segmentation in cluttered scenes. In a highly cited 2023 paper (19 citations), Deng introduced the Motion Forecasting Network (MoFCNet), which uses IMU data to accurately predict human motion intention—a critical capability for hip assistive exoskeletons that must respond seamlessly to user intent. Deng also pioneered the task of Class Incremental Robotic Pick-and-Place (CIRPAP), addressing the challenge of learning to handle new object categories without forgetting previously learned skills, a breakthrough for lifelong robotic learning. More recently, Deng’s work on Instance-Guided Net (IGN) for 3D instance segmentation via monocular depth sensors tackles the difficult problem of segmenting objects in cluttered, real-world scenes. With a growing citation impact and a focus on practical, deployable solutions, Deng is shaping the future of adaptive, assistive, and autonomous robotic systems.