New review in iOptics demonstrates how AI is overcoming key challenges in metasurface design, enabling advanced applications in compact optics and computational imaging.

Reactive planning and control capacity for collaborative robots is essential when the tasks change online in an unstructured environment. This is more difficult for collaborative mobile manipulators (CMM) due to high redundancies. To this end, this paper proposed a reactive whole-body locomotion-integrated manipulation approach based on combined learning and optimization. First, human demonstrations are collected, where the wrist and pelvis movements are treated as whole-body trajectories, mapping to the end-effector (EE) and the mobile base (MB) of CMM, respectively. A time-input kernelized movement primitive (T-KMP) learns the whole-body trajectory, and a multi-dimensional kernelized movement primitive (M-KMP) learns the spatial relationship between the MB and EE pose. According to task changes, the T-KMP adapts the learned trajectories online by inserting the new desired point predicted by M-KMP. Then, the updated reference trajectories are sent to a hierarchical quadratic programming (HQP) controller, where the EE and the MB trajectories tracking are set as the first and second priority tasks, generating the feasible and optimal joint level commands. An ablation simulation experiment with CMM of the HQP is conducted to show the necessity of MB trajectory tracking in mimicking human whole-body motion behavior. Finally, the tasks of the reactive pick-and-place and reactive reaching were undertaken, where the target object was randomly moved, even out of the region of demonstrations. The results showed that the proposed approach can successfully transfer and adapt the human whole-body loco-manipulation skills to CMM online with task changes.

The development of deep learning has motivated the advancement of unconventional computing that leverages analog physical systems such as analog electronics, spintronics, and photonics. These technologies have also led to the development of unique computational paradigms harnessing the features of analog devices, including compute-in-memory for nonvolatile devices and compute-in-sensor for analog electronics. What, then, are the computational paradigms that can exploit the characteristics of photonics? Optical computing has emerged as a promising candidate as it offers low-latency and low-power computation by utilizing the inherent parallelism of light. Additionally, the low-loss medium of optical fibers allows for the transmission of information over long distances. In this study, a remotely driven optical neural network that combines these advantageous features is demonstrated. Namely, computations can be executed with data transfer over a photonic network, which provides a computational paradigm named photonic compute-in-wire. As a proof-of-concept, an optoelectronic benchtop with a 20-km fiber access line are constructed, confirming good classification accuracy for image recognition tasks. The reported approach broadens the opportunities to utilize optical computation from local edge computing to in-network computing for low-latency and low-energy computation.

Embodied learning for object-centric robotic manipulation is a rapidly developing and challenging area in embodied AI. It is crucial for advancing next-generation intelligent robots and has garnered significant interest recently. Unlike data-driven machine learning methods, embodied learning focuses on robot learning through physical interaction with the environment and perceptual feedback, making it especially suitable for robotic manipulation. In this paper, researchers provide a comprehensive survey of the latest advancements in this field and categorize the existing work into three main branches: 1) Embodied perceptual learning, which aims to predict object pose and affordance through various data representations; 2) Embodied policy learning, which focuses on generating optimal robotic decisions using methods such as reinforcement learning and imitation learning; 3) Embodied task-oriented learning, designed to optimize the robot′s performance based on the characteristics of different tasks in object grasping and manipulation. In addition, researchers offer an overview and discussion of public datasets, evaluation metrics, representative applications, current challenges, and potential future research directions. A project associated with this survey has been established at https://github.com/RayYoh/OCRM_survey.

Researchers from Zhejiang Lab have proposed a novel spatiotemporal mode multiplexing technology, coupling pulsed orbital angular momentum (OAM) beams with diffractive deep neural networks (D²NN) and optical fiber delay-line arrays to realize a “space-time” projection mechanism. This enables high-speed, all-optical multidimensional multiplexing with potential scalability to the GHz level, offering a breakthrough solution for large-capacity optical communications in challenging environments such as free space and underwater.