Robotics Thesis Proposal
- Remote Access - Zoom
- Virtual Presentation
- ANKIT BHATIA
- Ph.D. Student
- Robotics Institute
- Carnegie Mellon University
Direct-drive Hands: Making Robot Hands Transparent and Reactive to Contact
Industrial manipulators and end-effectors are a vital driver of the automation revolution. These robot hands, designed to reject disturbances with stiffness and strength, are inferior to their human counterparts. Human hands are dexterous and nimble effectors capable of a variety of interactions with the environment.
Through this thesis we wish to answer a question: How can we make robot grippers better at interacting with the environment? A key idea essential for supporting a wide range of interactions with the environment is transparency: the efficient transfer of force and motion between the robot and the task. In the process we explore the key developments that allow robots to be reactive to contacts by analyzing the space of actuators and determine parameters that affects transparency. We identify properties of systems that affect transparency and propose to characterize transparency by the collision reflex response: the impulse transferred to a rigid, fixed object in a collision. We show that direct- drive is a favorable transmission paradigm for improving transparency. Recent advances in motor technology have triggered a resurgence in direct-drive and quasi-direct-drive systems. The MIT Cheetah, Ghost Robotics Minitaur, Agility Robotics Cassie are all examples of successful legged robots that have improved transparency without compromising their torque specifications.
We have developed the DDhand, a transparent direct-drive gripper inspired by the direct-drive robots from the 80’s. The DDHand is a 4 degree of freedom, two fingered gripper with a parallel five bar linkage to keep link reflected inertias low. The gripper improves on speed, bandwidth and mechanical simplicity over other comparable grippers.
Transparency in robotic grippers allows for new approaches in intrinsic contact sensing, and performance gains in contact-rich and dynamic behaviors. We show a velocity-based contact localization algorithm that can localize contact locations using only position and velocity sensing. We present the smack-and-snatch grasp: a contact-informed grasping behavior that can grasp an object on a table of unknown height in a rapid arm motion. We propose to develop new, performant manipulation behaviors; and demonstrate the viability of the gripper in industrial applications.
Matthew T. Mason (Co-Chair)
Aaron M. Johnson (Co-Chair)
Scott A. Bortoff (Mitsubishi Electric Research Labs)
Zoom Participation. See announcement.