Task-aware navigation continues to be a challenging area of research, especially in scenarios involving open vocabulary. Previous studies primarily focus on finding suitable locations for task completion, often overlooking the importance of the robot’s pose. However, the robot’s orientation is crucial for successfully completing tasks because of how objects are arranged (e.g., to open a refrigerator door). Humans intuitively navigate to objects with the right orientation using semantics and common sense. For instance, when opening a refrigerator, we naturally stand in front of it rather than to the side. Recent advances suggest that Vision-Language Models (VLMs) can provide robots with similar common sense. Therefore, we develop a VLM-driven method called Navigation-to-Gaze (Navi2Gaze) for efficient navigation and object gazing based on task descriptions. This method uses the VLM to score and select the best pose from numerous candidates automatically. In evaluations on multiple photorealistic simulation benchmarks, Navi2Gaze significantly outperforms existing approaches by precisely determining the optimal orientation relative to target objects, resulting in a 68.8% reduction in Distance to Goal (DTG). Real-world video demonstrations can be found on the supplementary website1.

This paper presents a 1st-order extended-counting (EC) incremental analog-to-digital converter (IADC) based on dynamic amplifier (DA). The wide output swing and high gain requirement of the amplifier makes it infeasible to design an energy-efficient high-resolution EC IADC based on DA. Conventional gain-boosting methods, such as multi-stage and cascoding, suffer from the stability issue and limited output swing respectively. To solve this problem, a foreground gain calibration method is proposed for reducing the gain requirement by compensating the quantization error caused by the limited gain of DA. Additionally, a two-stage DA is proposed to achieve wide output swing and high energy-efficiency, which combines the floating-inverter-based amplifier (FIA) and the switched-inverter-based amplifier (SIA). SIA works as the 2nd stage, providing wide output swing while FIA as the 1st stage maintains the gain > 50 dB. A prototype of 1st-order EC IADC based on the proposed DA with gain calibration is fabricated in 180-nm CMOS technology. Operating under 1.8-V power supply, the EC IADC achieves a SNDR/DR of 94.6 dB/ 98.1 dB and 20μW power consumption at 921 kHz offs, leading to a Schreier figure-of-merit (FOMSNDR) of 174.1 dB.

A high-speed low-noise comparator with an auxiliary inverter-based (AIB) preamplifier is proposed in this brief. The preamplifier adopts an inverter-based input pair without tail transistors, which is well-suitable for low-supply-voltage applications, especially in deep submicrometer technologies. Moreover, it achieves high bandwidth and low noise with high current efficiency. Dynamic common-mode feedback combined with a secondary inverter-based input pair regulates the output common-mode (CM) voltage of the preamplifier by itself against PVT and input CM voltage sensitivity without complex control logic and quiescent current. Embedded in a 200 MS/s 16-bit successive-approximation-register (SAR) analog-to-digital converter (ADC) in standard 28-nm CMOS technology, the proposed comparator achieves a 68.1-ps delay at a 50-$\mu \text{V}$differential input voltage and a root-mean-square input-referred noise of 45.6$\mu \text{V}_{\mathrm {rms}}$under the 1-V power supply.

How to design an optimal wearable device for human movement recognition is vital to reliable and accurate human-machine collaboration. Previous works mainly fabricate wearable devices heuristically. Instead, this paper raises an academic question: can we design an optimization algorithm to optimize the fabrication of wearable devices such as figuring out the best sensor arrangement automatically? Specifically, this work focuses on optimizing the placement of Forcemyography (FMG) sensors for FMG armbands in the application of arm movement recognition. Firstly, based on graph theory, the arm-band is modeled considering sensors' signals and connectivity. Then, a Graph-based Armband Modeling Network (GAM-Net) is introduced for arm movement recognition. Afterward, the sensor placement optimization for FMG armbands is formu-lated and an optimization algorithm with greedy local search is proposed. To study the effectiveness of our optimization algorithm, a dataset for mechanical maintenance tasks using FMG armbands with 16 sensors is collected. Our experiments show that using only 4 sensors optimized with our algorithm can help maintain a comparable recognition accuracy to using all sensors. Finally, the optimized sensor placement result is verified from a physiological view. This work would like to shed light on the automatic fabrication of wearable devices considering downstream tasks, such as human biological signal collection and movement recognition.

In this study, a passive arm-support exoskeleton was designed to provide assistive aid for manufacturing workers. The exoskeleton has two operating states which can be altered using an unique ratchet bar mechanism with two blocks fixed on the ratchet bar. When the upper arm is elevated to the highest poiont, the pawl module will touch the lower block to allow the pawl separated, so that the arm can move freely without any resistance. When the upper arm is depressed to the lowest point, the pawl module will touch the upper block to make the pawl re-engaged, so that the upper arm can be locked at any vertical position. For purpose to improve the ergonomical property, the structural parameters of the exoskeleton were determined by particle swarm optimization. The designed exoskeleton was simulated in the Adams model to investigate its actual performance. A preliminary experimental study was conducted to evaluate the effectiveness of the designed exoskeleton on alleviating users' physical loads in holding heavy tools; the muscular activities on the shoulder muscle groups involved in the weights bearing, elicited by the surface electromyography (EMG) over the shoulder, were significantly reduced from three healthy subjects who carried hand-held tools. The simulation and experiment results show that the designed exoskeleton could effectively relieve the shoulder burden by transferring the bearing load to the waist, where the motion of the arm was not obstructed.

In this study, a human-chair model was developed as the basis for a wearable chair design. A prototype chair, HUST-EC, was fabricated and evaluated. Employing the optimization under an inner point penalty function, an optimized simulation of the operating mode with the lowest chair height was implemented. The solid models were established by using the finite element analysis program embedded in Solidworks, which revealed that the support from the designed chair was steady to the user. An electromyography (EMG) test platform has been developed, consisting of four EMG sensors, a MATLAB-based acquisition software, and a loaded vest. Four healthy subjects participated in the evaluation experiment, in which EMGs were collected from the muscle groups of rectus femoris, biceps femoris, vastus medialis, and vastus lateralis under different loads and chair angles. The experimental data demonstrate that (1) the HUST-EC can greatly reduce muscle activation at a variety of loads and bending angles; (2) under the same load, the muscle activation decreases slightly with an increased bending angle; and (3) at the same bending angle, muscle activation increases slightly with an increased load. The results show that the designed chair can effectively reduce the physical burden in workers and may improve work efficiency.