This National Robotics Initiative 2.0 award supports fundamental research on fast, high-dimensional, and scalable sensing and modeling methods for soft grippers. The research will create soft grippers with significantly improved ability to handle objects in complicated environments. Soft grippers are constructed from flexible and soft materials that passively adapt to external forces, making them intrinsically safe for collaborating with humans and for handling delicate objects such as fruits and vegetables. Soft materials deform easily in response to applied forces, making them promising candidates for self-sensing. This project harnesses that promise, using embedded cameras and sophisticated algorithms to translate complex images into quantitative configuration and contact force information. Self-sensing enables soft grippers that are not limited to a preset passive response but can actively modify their operation according to their status. The active soft grippers arising from this project will find application in fields such as food industries, agriculture, assisted living for senior citizens or people with disabilities, increasing productivity and improving the quality of human life. The project follows a convergent research approach involving robotics and artificial intelligence, culminating in formal and informal learning activities to broaden the participation of underrepresented groups in engineering.
This award supports the development of DeepSoRo as a new framework of integrated proprioceptive and tactile sensing using embedded cameras to provide high-dimensional sensory input, and advanced deep learning models of the gripper’s full-body kinematics and dynamics. This framework will overcome the key limitations of existing soft grippers in modeling and sensing of their own states, including the over-simplified low-resolution representation, low-speed, and difficulty in scalability and adaptability to various gripper designs. To unleash the full potential of soft grippers, several scientific boundaries must be pushed, ensuring more holistic situational awareness of those grippers to perform dexterous and safe manipulations in complex environments. This research will fill critical knowledge gaps in soft robot sensing, sensor design, and deep learning, to realize the online shape estimation and feedback control of soft grippers, especially when the grippers are in contact with external objects. This interdisciplinary research program will unfold along three directions: high dimensional shape modeling in a latent space, joint proprioceptive and tactile sensing, and sensor design and integration in hardware prototypes. Theoretical advancements will proceed alongside with experimental research toward demonstrating the potential of DeepSoRo to accurately and efficiently model and sense soft grippers in real-world settings.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
