Improving Robotic Assistant Dexterity
As robotic assistants operate in complex, unstructured human-centered environments it becomes essential that robots
- Have access to as much useful data as possible as they interact with the environment
- Be able to leverage that data using intelligent models of the environment
Challenges for long term deployable robotic systems are
- Being able to handle uncertainty in a known environment
- Being able to reason about the properties of a new object/element in their environment
- Recovering from failure to complete a task/objective
By equipping robots to handle these challenges, we can develop robotic platforms that are capable of sustained, reliable, long-term service deployment.
Research objectives include:
- Design: Can we leverage anthropomorphic inspiration in a curved fingertip capable of deforming and sensing friction?
- Modeling: Can we efficiently model a continuous curved fingertip with high resolution (in a computationally tractable way) and characterize stable grasps?
- Motion Planning: Can we leverage these efficient, continuous models of our fingertips to plan multi-finger grasps for in-hand manipulation?
- Won Kyung Do
- Bianca Jurewicz
- Camille Chungyoun
- Jose Solano
- Karina Ting
- Tejas Yogesh
- Ankush Dhawan
- Mathilda Kitzmann
- DenseTact-Mini: An Optical Tactile Sensor for Grasping Multi-Scale Objects From Flat Surfaces (preprint)
- paper website: https://sites.google.com/view/densetact-mini/
- Inter-finger Small Object Manipulation with DenseTact Optical Tactile Sensor (preprint)
- paper website: https://sites.google.com/view/inter-finger-manipulation
- Embedded Object Detection and Mapping in Soft Materials using Optical Tactile Sensing (preprint)
- DenseTact 2.0: Optical Tactile Sensor for Shape and Force Reconstruction
- DenseTact: Optical Tactile Sensor for Dense Shape Reconstruction