Knowledge-integrated machine learning for materials: lessons from gameplaying and robotics

K Hippalgaonkar, Q Li, X Wang, JW Fisher III… - Nature Reviews …, 2023 - nature.com
As materials researchers increasingly embrace machine-learning (ML) methods, it is natural
to wonder what lessons can be learned from other fields undergoing similar developments …

Flexible and stretchable light-emitting diodes and photodetectors for human-centric optoelectronics

S Chang, JH Koo, J Yoo, MS Kim, MK Choi… - Chemical …, 2024 - ACS Publications
Optoelectronic devices with unconventional form factors, such as flexible and stretchable
light-emitting or photoresponsive devices, are core elements for the next-generation human …

Edge artificial intelligence for 6G: Vision, enabling technologies, and applications

KB Letaief, Y Shi, J Lu, J Lu - IEEE Journal on Selected Areas …, 2021 - ieeexplore.ieee.org
The thriving of artificial intelligence (AI) applications is driving the further evolution of
wireless networks. It has been envisioned that 6G will be transformative and will …

Making sense of vision and touch: Learning multimodal representations for contact-rich tasks

MA Lee, Y Zhu, P Zachares, M Tan… - IEEE Transactions …, 2020 - ieeexplore.ieee.org
Contact-rich manipulation tasks in unstructured environments often require both haptic and
visual feedback. It is nontrivial to manually design a robot controller that combines these …

Polytransform: Deep polygon transformer for instance segmentation

J Liang, N Homayounfar, WC Ma… - Proceedings of the …, 2020 - openaccess.thecvf.com
In this paper, we propose PolyTransform, a novel instance segmentation algorithm that
produces precise, geometry-preserving masks by combining the strengths of prevailing …

Rh20t: A comprehensive robotic dataset for learning diverse skills in one-shot

HS Fang, H Fang, Z Tang, J Liu, C Wang… - … on Robotics and …, 2024 - ieeexplore.ieee.org
A key challenge for robotic manipulation in open domains is how to acquire diverse and
generalizable skills for robots. Recent progress in one-shot imitation learning and robotic …

Rh20t: A robotic dataset for learning diverse skills in one-shot

HS Fang, H Fang, Z Tang, J Liu, J Wang… - RSS 2023 Workshop …, 2023 - openreview.net
A key challenge in learning task and motion planning in open domains is how to acquire
diverse and generalizable skills for robots. Recent research in one-shot imitation learning …

Cog: Connecting new skills to past experience with offline reinforcement learning

A Singh, A Yu, J Yang, J Zhang, A Kumar… - arXiv preprint arXiv …, 2020 - arxiv.org
Reinforcement learning has been applied to a wide variety of robotics problems, but most of
such applications involve collecting data from scratch for each new task. Since the amount of …

Contactnets: Learning discontinuous contact dynamics with smooth, implicit representations

S Pfrommer, M Halm, M Posa - Conference on Robot …, 2021 - proceedings.mlr.press
Common methods for learning robot dynamics assume motion is continuous, causing
unrealistic model predictions for systems undergoing discontinuous impact and stiction …

Intelligent Recognition Using Ultralight Multifunctional Nano-Layered Carbon Aerogel Sensors with Human-Like Tactile Perception

H Zhao, Y Zhang, L Han, W Qian, J Wang, H Wu, J Li… - Nano-Micro Letters, 2024 - Springer
Humans can perceive our complex world through multi-sensory fusion. Under limited visual
conditions, people can sense a variety of tactile signals to identify objects accurately and …