ΑΙhub.org
Making sense of vision and touch: #ICRA2019 best paper award video and interview
PhD candidate Michelle A. Lee from the Stanford AI Lab won the best paper award at ICRA 2019 with her work “Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks”. You can read the paper on arxiv here.
Audrow Nash was there to capture her pitch.
And here’s the official video about the work.
Full reference
Lee, Michelle A., Yuke Zhu, Krishnan Srinivasan, Parth Shah, Silvio Savarese, Li Fei-Fei, Animesh Garg, and Jeannette Bohg. “Making sense of vision and touch: Self-supervised learning of multimodal representations for contact-rich tasks.” arXiv preprint arXiv:1810.10191 (2018).
AIhub
is dedicated to free high-quality information about AI.
AIhub
is dedicated to free high-quality information about AI.
AIhub is supported by:
Related posts :
Machine learning for atomic-scale simulations: balancing speed and physical laws
How much underlying physics can we safely “shortcut” without breaking a simulation?
Policy design for two-sided platforms with participation dynamics: Interview with Haruka Kiyohara
Lucy Smith
09 Oct 2025
Studying the long-term impacts of decision-making algorithms on two-sided platforms such as e-commerce or music streaming apps.
The Machine Ethics podcast: What excites you about AI? Vol.2
The Machine Ethics Podcast
08 Oct 2025
This is a bonus episode looking back over answers to our question: What excites you about AI?
Interview with Janice Anta Zebaze: using AI to address energy supply challenges
Lucy Smith
07 Oct 2025
Find out more about research combining renewable energy systems, tribology, and artificial intelligence.
How does AI affect how we learn? A cognitive psychologist explains why you learn when the work is hard
The Conversation
06 Oct 2025
Early research is only beginning to scratch the surface of how AI technology will truly affect learning and cognition in the long run.
Interview with Zahra Ghorrati: developing frameworks for human activity recognition using wearable sensors
Lucy Smith
03 Oct 2025
Find out more about research developing scalable and adaptive deep learning frameworks.
Diffusion beats autoregressive in data-constrained settings
ML@CMU
03 Oct 2025
How can we trade off more compute for less data?
Forthcoming machine learning and AI seminars: October 2025 edition
Lucy Smith
02 Oct 2025
A list of free-to-attend AI-related seminars that are scheduled to take place between 3 October and 30 November 2025.