16 citations · 58 across the 15 of their papers we have counts for
15 papers
Will People Enjoy a Robot Trainer? A Case Study with Snoopie the Pacerbot
Maximilian Du, Jennifer Grannen, Shuran Song +1
The physicality of exercise makes the role of athletic trainers unique. Their physical presence allows them to guide a student through a motion, demonstrate an exercise, and give i…
DexDrummer: In-Hand, Contact-Rich, and Long-Horizon Dexterous Robot Drumming
Hung-Chieh Fang, Amber Xie, Jennifer Grannen +2
Performing in-hand, contact-rich, and long-horizon dexterous manipulation remains an unsolved challenge in robotics. Prior hand dexterity works have considered each of these three…
Robot-Powered Data Flywheels: Deploying Robots in the Wild for Continual Data Collection and Foundation Model Adaptation
Jennifer Grannen, Michelle Pan, Kenneth Llontop +4
Foundation models (FM) have unlocked powerful zero-shot capabilities in vision and language, yet their reliance on internet pretraining data leaves them brittle in unstructured, re…
ProVox: Personalization and Proactive Planning for Situated Human-Robot Collaboration
Jennifer Grannen, Siddharth Karamcheti, Blake Wulfe +1
Collaborative robots must quickly adapt to their partner's intent and preferences to proactively identify helpful actions. This is especially true in situated settings where human…
A Study of Perceived Safety for Soft Robotics in Caregiving Tasks
Cosima du Pasquier, Jennifer Grannen, Chuer Pan +6
In this project, we focus on human-robot interaction in caregiving scenarios like bathing, where physical contact is inevitable and necessary for proper task execution because forc…
Vocal Sandbox: Continual Learning and Adaptation for Situated Human-Robot Collaboration
Jennifer Grannen, Siddharth Karamcheti, Suvir Mirchandani +2
We introduce Vocal Sandbox, a framework for enabling seamless human-robot collaboration in situated environments. Systems in our framework are characterized by their ability to ada…
Stabilize to Act: Learning to Coordinate for Bimanual Manipulation
Jennifer Grannen, Yilin Wu, Brandon Vu +1
Key to rich, dexterous manipulation in the real world is the ability to coordinate control across two hands. However, while the promise afforded by bimanual robotic systems is imme…
Learning Bimanual Scooping Policies for Food Acquisition
Jennifer Grannen, Yilin Wu, Suneel Belkhale +1
A robotic feeding system must be able to acquire a variety of foods. Prior bite acquisition works consider single-arm spoon scooping or fork skewering, which do not generalize to f…
In-Mouth Robotic Bite Transfer with Visual and Haptic Sensing
Lorenzo Shaikewitz, Yilin Wu, Suneel Belkhale +3
Assistance during eating is essential for those with severe mobility issues or eating risks. However, dependence on traditional human caregivers is linked to malnutrition, weight l…
Detecting Damage Building Using Real-time Crowdsourced Images and Transfer Learning
Gaurav Chachra, Qingkai Kong, Jim Huang +4
After significant earthquakes, we can see images posted on social media platforms by individuals and media agencies owing to the mass usage of smartphones these days. These images…
Untangling Dense Non-Planar Knots by Learning Manipulation Features and Recovery Policies
Priya Sundaresan, Jennifer Grannen, Brijen Thananjeyan +7
Robot manipulation for untangling 1D deformable structures such as ropes, cables, and wires is challenging due to their infinite dimensional configuration space, complex dynamics,…
Disentangling Dense Multi-Cable Knots
Vainavi Viswanath, Jennifer Grannen, Priya Sundaresan +7
Disentangling two or more cables requires many steps to remove crossings between and within cables. We formalize the problem of disentangling multiple cables and present an algorit…
Untangling Dense Knots by Learning Task-Relevant Keypoints
Jennifer Grannen, Priya Sundaresan, Brijen Thananjeyan +7
Untangling ropes, wires, and cables is a challenging task for robots due to the high-dimensional configuration space, visual homogeneity, self-occlusions, and complex dynamics. We…
Learning Rope Manipulation Policies Using Dense Object Descriptors Trained on Synthetic Depth Data
Priya Sundaresan, Jennifer Grannen, Brijen Thananjeyan +5
Robotic manipulation of deformable 1D objects such as ropes, cables, and hoses is challenging due to the lack of high-fidelity analytic models and large configuration spaces. Furth…
Learning Dense Visual Correspondences in Simulation to Smooth and Fold Real Fabrics
Aditya Ganapathi, Priya Sundaresan, Brijen Thananjeyan +10
Robotic fabric manipulation is challenging due to the infinite dimensional configuration space, self-occlusion, and complex dynamics of fabrics. There has been significant prior wo…