Robot adapts touch to read braille with high accuracy
Can a Robot Read Braille? - Learning to Adapt Contact via Imitation Learning for Tactile Braille Recognition
Robotics
Summary
Reading Braille by touch is a key way blind people access information. To help robots do this, the authors created a method that not only recognizes Braille patterns but also adjusts how the robot's finger touches the surface to get clearer readings. They taught the robot using examples from experts on how to adjust the touch and then tested it on real Braille plates, achieving high accuracy. This shows that robots need to actively fix their touch position before reading Braille, rather than just trying to recognize patterns after a poor contact.
What this means in practice
- •For assistive robotics developers: Build robots that can reliably read Braille by adjusting their touch to ensure clear tactile inputs before recognition.
- •For robotic manipulation engineers: Design robotic fingers that adapt their positioning dynamically based on touch quality feedback for improved tactile sensing tasks beyond Braille.
Authors
Xi Chen, Yunlong Shan, Sihan Chen, Jun Hu, Zhongxuan Li, Shiyao Zhang, Sichao Liu, Zhong Zhao, Kosta Jovanovic, Peng Zhou
Abstract
For people who are blind, touch provides an essen-tial channel for accessing written information through Braille. Bringing a similar capability to robots requires them not only to recognize tactile patterns, but also to actively establish physical contact that makes those patterns readable. Yet existing robotic Braille readers largely focus on recognition after contact, leaving contact establishment itself insufficiently addressed. We present an adaptive-contact framework for robotic tactile Braille reading that assesses contact quality and physically corrects unsuitable contact before recognition and reconstruc-tion. Multi-Head Policy Learning uses expert-guided contact-adjustment demonstrations to jointly learn contact acceptability and pose corrections. During deployment, the robot iteratively evaluates and re-establishes contact, retaining reliable tactile observations for pose-aware fusion and Braille reconstruction. Across 20 physical Braille plates used for learning and eval-uation, the proposed approach achieves 94.0% tactile quality and 88.6% tactile reconstruction on the ten online-evaluation plates. These results demonstrate the importance of actively establishing readable contact, rather than relying solely on recognition under imperfect tactile observations, for reliable robotic Braille reading.