1 citations · 3 across the 8 of their papers we have counts for
10 papers
Robust Camera-to-Mocap Calibration and Verification for Large-Scale Multi-Camera Data Capture
Tianyi Liu, Christopher Twigg, Patrick Grady +3
Optical motion capture (mocap) systems are widely used for ground-truth capture in AR/VR, SLAM and robotics datasets. These datasets require extrinsic calibration to align mocap co…
The Un-Kidnappable Robot: Acoustic Localization of Sneaking People
Mengyu Yang, Patrick Grady, Samarth Brahmbhatt +3
How easy is it to sneak up on a robot? We examine whether we can detect people using only the incidental sounds they produce as they move, even when they try to be quiet. We collec…
Visual Contact Pressure Estimation for Grippers in the Wild
Jeremy A. Collins, Cody Houff, Patrick Grady +1
Sensing contact pressure applied by a gripper can benefit autonomous and teleoperated robotic manipulation, but adding tactile sensors to a gripper's surface can be difficult or im…
PressureVision++: Estimating Fingertip Pressure from Diverse RGB Images
Patrick Grady, Jeremy A. Collins, Chengcheng Tang +4
Touch plays a fundamental role in manipulation for humans; however, machine perception of contact and pressure typically requires invasive sensors. Recent research has shown that d…
Force/Torque Sensing for Soft Grippers using an External Camera
Jeremy A. Collins, Patrick Grady, Charles C. Kemp
Robotic manipulation can benefit from wrist-mounted force/torque (F/T) sensors, but conventional F/T sensors can be expensive, difficult to install, and damaged by high loads. We p…
Visual Pressure Estimation and Control for Soft Robotic Grippers
Patrick Grady, Jeremy A. Collins, Samarth Brahmbhatt +4
Soft robotic grippers facilitate contact-rich manipulation, including robust grasping of varied objects. Yet the beneficial compliance of a soft gripper also results in significant…
PressureVision: Estimating Hand Pressure from a Single RGB Image
Patrick Grady, Chengcheng Tang, Samarth Brahmbhatt +4
People often interact with their surroundings by applying pressure with their hands. While hand pressure can be measured by placing pressure sensors between the hand and the enviro…
BodyPressure -- Inferring Body Pose and Contact Pressure from a Depth Image
Henry M. Clever, Patrick Grady, Greg Turk +1
Contact pressure between the human body and its surroundings has important implications. For example, it plays a role in comfort, safety, posture, and health. We present a method t…
ContactOpt: Optimizing Contact to Improve Grasps
Patrick Grady, Chengcheng Tang, Christopher D. Twigg +3
Physical contact between hands and objects plays a critical role in human grasps. We show that optimizing the pose of a hand to achieve expected contact with an object can improve…
Learning to Collaborate from Simulation for Robot-Assisted Dressing
Alexander Clegg, Zackory Erickson, Patrick Grady +3
We investigated the application of haptic feedback control and deep reinforcement learning (DRL) to robot-assisted dressing. Our method uses DRL to simultaneously train human and r…