This $454,020 Project Grant awarded by the National Science Foundation (NSF) Engineering program (CFDA 47.041) will support collaborative research at the University of Michigan to develop soft materials with embedded sensors, circuits, and actuators enabling a new class of intelligent soft robots. The goal is to create a soft robotic manipulator that can autonomously recognize and sort objects by their physical characteristics using only these advanced material components, without requiring...
This $150,000 Project Grant award from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) supports fundamental research at the Rochester Institute of Technology (RIT) to develop new robotic manipulation capabilities for safely and reliably handling fragile objects. The research aims to establish an understanding of the synergy between vision and touch sensing, and to create a control method based on object properties that can optimize robotic manipulation for a variety...
This Project Grant awarded by the National Science Foundation's (NSF) Engineering program (CFDA 47.041) will fund the development of advanced, vision-based tactile gel-robots capable of perceiving and manipulating soft, fragile objects. The $312,960 award will integrate fatigue-resistant photoelastic gels, stress-interpreting photometry systems, and physics-informed machine learning to enable multi-physical perception and ultra-gentle handling capabilities. This research will advance robotics in...
This $499,076 National Science Foundation (NSF) grant awarded under the CFDA 47.041 Engineering program aims to develop efficient computational methods for robots to learn reliable dexterous manipulation skills without relying on significant human effort or computational resources. The principal objectives are to: 1) Learn dexterous manipulation skills from observations, 2) Learn multi-sensory representations and models from self-guided play, and 3) Analyze and guarantee learned manipulation...
This $272,617 National Science Foundation (NSF) Engineering program (CFDA 47.041) award to the University of Illinois supports fundamental research on high-dimensional proprioceptive and tactile sensing methods for soft robotic grippers. The project aims to develop a new framework, called DeepSORO, that uses embedded cameras and advanced deep learning models to provide real-time, high-resolution sensing and state estimation of the grippers' kinematics and dynamics. This research seeks to...
This five-year, $205,073 Project Grant from the National Science Foundation's Computer and Information Science and Engineering program (CFDA 47.070) will support research at Tufts University to develop methods for transferring multisensory object knowledge across collaborative robots. The Principal Investigator will work to enable robots with different bodies, sensors and movement capabilities to learn from each other's experiences manipulating and perceiving objects through various senses. This...
This $140,000 Project Grant award from the National Science Foundation's (NSF) Engineering program (CFDA 47.041) aims to revolutionize teleoperation technology for dexterous in-hand manipulation in human-robot collaboration. The primary research objectives are to: 1) develop a learning-based robot control policy to interpret human commands using end-effect-based task features, 2) create a safety-aware, multi-modal perception system to optimize sensory inputs for intuitive and safe operation, and...
This $50,000 Project Grant was awarded by the National Science Foundation (NSF) Engineering program (CFDA 47.041) to The Leland Stanford Junior University (Stanford University) to develop a novel approach for robotic dexterous manipulation that leverages the powerful new object representation of neural radiance fields (NeRFs). The primary objective is to create an algorithmic pipeline that can go from camera pixels to grasp to manipulation trajectory, enabling complex manipulation skills such as...
The National Science Foundation Division of Computer and Network Systems awarded a $337,020 project grant to Stanford University under the Computer and Information Science and Engineering program (CFDA 47.070) to support research on visual tactile neural fields for active digital twin generation. Over a three-year period from October 2022 through September 2025, Stanford researchers will develop new algorithms and computational tools to allow robots to combine visual and tactile sensory data...
This four-year, $603,183 Project Grant from the National Science Foundation's Computer and Information Science and Engineering program (CFDA 47.070) will support the development of shape-based remote manipulation technologies at Northwestern University. The goals of the research are to overcome limitations in haptic feedback and dexterous control for remote presence systems through innovations in multi-finger haptic interfaces, geometric modeling of grasped objects, and shape-based control...