Robotic hands perform full tool use with unified motion control
Relative Contact Velocity-Controlled Hand-Object Mechanism for Dexterous Tool Manipulation
Robotics
Summary
Robots with many fingers usually struggle to pick up and use tools like humans do. This paper presents a way to model the robot hand and the tool together so they move as one system, regardless of the robot’s shape. The authors created a planning system that uses this model to control hand movements and contact forces. They tested it with different robot hands and tools in simulation, showing it can handle the entire process of picking up and skillfully manipulating tools from simple commands.
What this means in practice
- •For robotic system integrators: Integrate a unified control framework that enables multiple types of robotic hands to pick up and manipulate tools dexterously in varied tasks.
- •For industrial automation engineers: Design flexible robot workstations that can switch between different tools and hands without redesigning control algorithms for each combination.
Authors
Sunyu Wang, Jean Oh, Nancy S. Pollard
Abstract
This work investigates how to enable general multi-finger robotic hands to perform the complete tool manipulation process, which entails picking up a tool, loading it into a suitable pose, and then wielding it. Inspired by human tool manipulation and mechanical design principles, we model the hand and the tool as a unified hand-object mechanism (HOM) composed of sub-assemblies. Specifically, we define a HOM as consisting of the hand, the object, and the generalized contact frames, allowing the HOM's motions to be expressed with the same set of Cartesian-space relative contact velocities, irrespective of the hand's kinematics and geometry. Then, we define a HOM's sub-assemblies as relative contact velocity and contact force constraints between fingers. Building on these definitions, we developed a lightweight and physically interpretable motion planning and contact estimation framework using least squares and a complementary filter. We evaluated our framework in simulation by teleoperating five different robotic hands. The results show that our framework enabled all five hands to execute the complete tool manipulation process, achieving dexterous behaviors even from identical, simple reference trajectories. Furthermore, the results showcase our framework's adaptability to different hands, tools, and tasks, enabled by its kinematic and geometric foundation.