The advent of simulation engines has revolutionized learning and operational efficiency for robots, offering cost-effective and swift pipelines. However, the lack of a universal simulation platform tailored for chemical scenarios impedes progress in robotic manipulation and visualization of reaction processes. Addressing this void, we present Chemistry3D, an innovative toolkit that integrates extensive chemical and robotic knowledge. Chemistry3D not only enables robots to perform chemical experiments but also provides real-time visualization of temperature, color, and pH changes during reactions. Built on the NVIDIA Omniverse platform, Chemistry3D offers interfaces for robot operation, visual inspection, and liquid flow control, facilitating the simulation of special objects such as liquids and transparent entities. Leveraging this toolkit, we have devised RL tasks, object detection, and robot operation scenarios. Additionally, to discern disparities between the rendering engine and the real world, we conducted transparent object detection experiments using Sim2Real, validating the toolkit's exceptional simulation performance. The source code is available at https://github.com/huangyan28/Chemistry3D, and a related tutorial can be found at https://www.omni-chemistry.com.

To realize the exquisite interaction and precise manipulation for the robot, in this article, we propose a multispectral multimodal visuotactile sensor named M$^{3}$Tac, which combines visible, near-infrared, and mid-infrared imaging technologies for the first time and can exceed the sensing ability of human skin in terms of resolution (719 pixels/cm$^{2}$), temperature sensing range (−20–130$^\text{o}$C), etc. The M$^{3}$Tac cannot only realize high-quality sensing of deformation, texture, force, stickiness, and temperature comparable to human skin but also can realize proximity sensing that is lacking for human skin. To achieve this, we not only design a multispectral imaging system with an elastic film whose light penetrability can be regulated by the brightness of the light, but also develop corresponding algorithms, including the pixel-level force sensing with finite element method (accuracy:$\pm$0.023N), the proximity perception (accuracy:$\pm$3.8 mm), the 3-D reconstruction (accuracy: 0.33 mm), the super-resolution temperature sensing (accuracy:$\pm 0.3^\text{o}$C), the multimodal fusion classification (accuracy: 98%), and the stickiness recognition (accuracy: 98%). Finally, we conduct experiments to verify the effectiveness and application potential of our research.