Robot hands learn to write in the air with a pen fast
Rapid Learning of Dexterous In-Hand Pen Writing through Real-Time Jacobian Estimation
Robotics
Summary
It is very hard for robot hands to skillfully move and write with a pen inside their grasp because the way the hand and pen touch and move is complex and hard to model. The authors found a way for a robot hand to quickly learn to write letters by estimating how the pen moves based on the hand’s real-time motion, without needing lots of training or simulations. This method works on different robot hands and produces writing with very high precision. It shows a new way to get robots to do delicate tasks like writing using fast and simple calculations.
What this means in practice
- •For robotics engineers: Develop robot hands capable of precise in-hand object articulation like pen writing without needing large training data or complex simulations.
- •For industrial automation teams: Automate fine motor tasks requiring dexterous object handling and manipulation with quick setup and adaptable robotic controllers.
Authors
Kai Stewart, Yasunori Toshimitsu, Robert K. Katzschmann
Abstract
Dexterous in-hand manipulation of a grasped object with an anthropomorphic hand is an unsolved frontier for robot dexterity. The contact-richness and highly dynamic nature of object-hand interactions tend to require extensive modeling or data-collection efforts for learning-based approaches. Modern simulators used for reinforcement learning (RL) cannot fully replicate the required contact complexity, while collecting dexterous demonstrations for imitation learning (IL) remains an open problem. In this research, we present an embodied control approach based on real-time task Jacobian estimation of the combined hand and object system on the physical robot. Using only the CPU on a laptop, the proposed controller begins in-hand pen writing after approximately 18 s of initialization and continues to adapt online, without an analytic hand--object kinematic/contact model, simulation training, or precollected task demonstrations. We demonstrate that the same estimator/controller formulation works on three anthropomorphic robotic hand systems (one physical, two simulated) to show human-like, in-hand articulation of a grasped pen by an embodiment-independent formulation. Sub-millimeter in-plane precision (mean 0.6 mm across runs) is achieved across letters and shapes written in the air and on paper on a physical robot. To our knowledge, this is the first demonstration of an anthropomorphic hand writing arbitrary single-stroke trajectories with a grasped pen through purely in-hand motion, and it showcases an alternative to compute- and data-heavy approaches such as RL and IL for achieving dexterous manipulation through computationally simple and data-efficient algorithms.