6 papers
Play2Perfect: What Matters in Dexterous Play Pretraining for Precise Assembly?
Tyler Ga Wei Lum, Kushal Kedia, C. Karen Liu +1
Multi-fingered robots promise the speed and dexterity of human hands, yet challenging problems such as precise assembly have remained out of reach. These tasks are contact-rich, ma…
SimToolReal: An Object-Centric Policy for Zero-Shot Dexterous Tool Manipulation
Kushal Kedia, Tyler Ga Wei Lum, Jeannette Bohg +1
The ability to manipulate tools significantly expands the set of tasks a robot can perform. Yet, tool manipulation represents a challenging class of dexterity, requiring grasping t…
Scaffolding Dexterous Manipulation with Vision-Language Models
Vincent de Bakker, Joey Hejna, Tyler Ga Wei Lum +6
Dexterous robotic hands are essential for performing complex manipulation tasks, yet remain difficult to train due to the challenges of demonstration collection and high-dimensiona…
Get a Grip: Multi-Finger Grasp Evaluation at Scale Enables Robust Sim-to-Real Transfer
Tyler Ga Wei Lum, Albert H. Li, Preston Culbertson +4
This work explores conditions under which multi-finger grasping algorithms can attain robust sim-to-real transfer. While numerous large datasets facilitate learning generative mode…
Neural Attention Field: Emerging Point Relevance in 3D Scenes for One-Shot Dexterous Grasping
Qianxu Wang, Congyue Deng, Tyler Ga Wei Lum +5
One-shot transfer of dexterous grasps to novel scenes with object and context variations has been a challenging problem. While distilled feature fields from large vision models hav…
DextrAH-G: Pixels-to-Action Dexterous Arm-Hand Grasping with Geometric Fabrics
Tyler Ga Wei Lum, Martin Matak, Viktor Makoviychuk +5
A pivotal challenge in robotics is achieving fast, safe, and robust dexterous grasping across a diverse range of objects, an important goal within industrial applications. However,…