Xianyi Cheng
Carnegie Mellon University
xianyic@andrew.cmu.edu
Bio
Xianyi Cheng is a Ph.D. student in the Department of Mechanical Engineering specializing in robotics at Carnegie Mellon University under the supervision of Professor Matthew T. Mason. She received a master’s degree in robotics from Carnegie Mellon University and a bachelor’s degree in astronautical engineering from Harbin Institute of Technology. Her primary research interests include mechanics planning and optimization in robotic manipulation. Specifically her current work focuses on generating versatile and robust dexterous manipulation skills. She is a recipient of a Foxconn Graduate Fellowship.
Bringing Human-level Dexterity into Robotic Manipulation
Bringing Human-level Dexterity into Robotic Manipulation
Human beings are highly creative in manipulating objects: grasping pushing pivoting manipulating with limbs using the environment as extra support and so on. In contrast robots lack such intelligence and capabilities for dexterous manipulation. My ultimate research goal is to bring human-level dexterity into robotic manipulation. Currently my work focuses on dexterous manipulation motion generation the first step towards general manipulation intelligence. In our recent works we considered the dexterity from planning through both the robot hand contacts and environment contacts. The contact-rich nature makes this problem challenging and unsolved. To address it we first studied the mechanics of contacts. We proved that the complexity of enumerating contact modes for one object is polynomial instead of exponential to the number of contacts. This exciting observation inspired us to use instantly enumerated contact modes as guidance to an RRT-based planner. Contact modes guide the motion generation like automatically generated motion primitives capturing all possible transitions of contacts in the system. As a result our planner can generate dexterous motions only given the start and goal of object poses. We also observed that some generated strategies resemble human manipulation behaviors such as the “simultaneous levering out and grasp formation” in human grasping. To the best of our knowledge our planner is the first method capable of solving diverse dexterous manipulation tasks without any pre-designed skill or pre-specified contact modes. For future work I continue to improve this framework by adding hierarchical structures and enabling robust dynamic behaviors.