Shuo Jiang PhD Dissertation Defense

Name:
Shuo Jiang

Title:
Tactile Intelligence in Robotics

Date:
8/16/2024

Time:
2:00:00 PM

Location:
EXP-701A

Committee Members:
Prof. Lawson Wong (Advisor)
Prof. Robert Platt
Prof. Alireza Ramezani
Prof. Taskin Padir

Abstract:
In recent years, the evolution of robot electronic skin technology has introduced a novel avenue for robots to perceive their external environment and internal state. In contrast to conventional visual perception methods, tactile perception enables the discernment of additional physical properties of objects, such as friction and mass distribution, or even observes contact with higher resolution. Importantly, tactile perception is resilient to challenges posed by inadequate illumination or environmental occlusion. However, it presents inherent challenges, including a limited sensing range, compulsory physical interaction with the environment, and intricate coupling with robot control, rendering data collection and utilization challenging. Addressing these challenges and devising effective, efficient, and interpretable methods for processing tactile signals have emerged as pivotal issues in robot tactile perception.

With the development of artificial intelligence technology, we are now able to interpret tactile information from a new perspective beyond traditional sensor technology and signal processing methods, thereby expanding a wider range of robotic applications. With our continuous efforts over the past few years, we have comprehensively addressed the following challenges in enhancing robot tactile perception through the application of advanced artificial intelligence and control methods: enabling robots to explore object shapes through tactile feedback; developing tactile-based safety mechanisms for human-robot collaboration; enhancing the locomotion adaptability of snake robots on irregular terrains through tactile perception; utilizing whole-body exteroceptors and proprioceptors for accurate body schema estimation; and implementing tactile gesture recognition in human-robot interactions. At the same time, we developed a modular full-body electronic skin system for robots and its accompanying software, which can accurately detect forces applied to the robot’s entire body and perform high-speed tracking of the real-time kinematics of the robot’s sensor array.

In conclusion, this dissertation explores how robot tactile perception can accomplish complex tasks in various scenarios or achieve performance improvements in traditional tasks through the integration of sensor technology, machine learning, control theory, and robotics. Through extensive theoretical and experimental analysis, we have demonstrated the critical role of tactile perception in embodied intelligence for robots and established a fundamental knowledge framework for future academic research in this field.