Samuel Clarke

Samuel Clarke is a Computer Science PhD student at Stanford University.13 He is currently being advised by Professor Jiajun Wu in Stanford's Computer Science Department.1 Prior to his doctoral studies, Clarke worked as a Graduate Research Assistant at Carnegie Mellon University's Robotics Institute, where he was co-advised by Professors Chris Atkeson and Oliver Kroemer.1

Research Focus

Clarke's research primarily revolves around learning for manipulation in robotics, with a particular emphasis on how robots can utilize sound during manipulation tasks.1 Some of his notable research areas include:

1. Acoustic-based Human Detection: Developing learning-based models that can track, identify, or detect humans in a room using minute differences in room acoustics.1

2. Soft-body Manipulation: Creating frameworks for long-horizon tasks in soft-body manipulation, such as making dumplings with various tools.1

3. Impact Sound Analysis: Collecting and analyzing large datasets of impact sounds from everyday objects to enhance acoustic simulations and audiovisual tasks.1

4. Audio-based Feedback in Robotics: Utilizing deep learning models to predict and control granular material manipulation based on audio feedback.1

Academic Performance

Clarke has demonstrated excellent academic performance:

• GPA: 4.17/4.33 (presumably for his graduate studies)
• GPA: 4.0/4.0 (likely for his undergraduate degree)1

Publications and Contributions

Samuel Clarke has contributed to several publications in his field. One notable publication is "OBJECTFOLDER 2.0: A Multisensory Object Dataset for Sim2Real Transfer," which was published by the IEEE Computer Society in 2022.2

Online Presence

Clarke maintains an active online presence related to his academic work:

• He has a Google Scholar profile where his publications are listed.4
• On Twitter, he describes himself as a "PhD Student in Computer Science at Stanford" working on "robot learning, especially learning from manipulation sounds".5

Samuel Clarke's research and academic pursuits demonstrate a strong focus on advancing the field of robotics, particularly in areas where sound and manipulation intersect, contributing to the development of more sophisticated and adaptable robotic systems.