Skills of cutting natural foods are important for robots looking to play a bigger role in kitchen assistance. The basic objective of cutting is to achieve material fracture via smooth movements of a kitchen knife, which in the process performs work to overcome material toughness, acts against blade-material friction, and generates shape deformation. This paper investigates how a robotic arm drives the knife to cut through an object in a sequence of three moves: pressing, touching, and slicing. To cope with evolving contacts with the material and cutting board, position, force, and impedance controls act either separately or jointly, assisted by force sensing and/or based on fracture mechanics, so the knife follows a prescribed trajectory to split the object. Force data acquired during the phase of pressing are used for estimating the object-specific values of physical parameters related to cutting. These estimated values are promptly used for control purpose to execute the phase of slicing. Experiments over several types of fruits and vegetables have exhibited natural cutting movements resembling those performed by a human hand.Note to Practitioners—Automation of kitchen skills is an important step in the development of home robots, which are expected to relieve us from daily chores and help us care for the elderly and people with disabilities. The motivation of this research is to enable a robotic arm to cut natural foods with knife movements that bear the smoothness and efficiency of those executed by a human hand. Existing methods on robotic cutting have focused on force control to ensure material separation but not on execution of natural knife movements. This paper dissects a cutting action into three phases, as inspired from the human hand execution, and realizes them via different control policies. These policies are based on sensing and modeling the forces experienced by the knife through its interactions with the material and the cutting board. Preliminary experimental results have demonstrated cutting of various food items with speed and smoothness. In future research, we will address cutting of deformable objects and explore issues including energy efficiency, cutting by the knife held in a robotic hand, and food stabilization and manipulation by a second arm/hand.

Multiple Input Multiple Output (MIMO) radar, a new radar system with waveform diversity, can improve detection performance. However, there are still challenges in the current MIMO radar target detection process, such as difficult waveform separation, high data demand, high algorithm complexity, and poor detection performance. To address these issues, this letter presents a Space-Time-Waveform Joint Adaptive Detection (STWJAD) method. By combining spatial, temporal, and waveform dimensions, the STWJAD is based on the Linearly Constrained Minimum Variance (LCMV) criterion to achieve effective adaptive processing and detection. Experimental results demonstrate that the proposed method can effectively suppress sidelobes, clutter and noise, exhibit excellent detection capabilities, and boast a lower data demand, faster processing speed.

Dexterous robotic cutting needs to demonstrate a skill level with smooth and efficient knife movements. The work performed by the knife mainly generates fracture and overcomes the blade-material friction. This paper presents a recursive least-squares method that repeatedly estimates relevant physical parameters such as Poisson's ratio, fracture toughness, and coefficient of friction, all varying with the knife's movement when cutting a natural food, from force sensor readings. Furthermore, we show that these estimates can be used for generating the knife's trajectory on the fly to either maximize the ease of fracturing or to minimize the rate of work.

As a classic deep learning target detection algorithm, Faster R-CNN (region convolutional neural network) has been widely used in high-resolution synthetic aperture radar (SAR) and inverse SAR (ISAR) image detection. However, for most common low-resolution radar plane position indicator (PPI) images, it is difficult to achieve good performance. In this paper, taking navigation radar PPI images as an example, a marine target detection method based on the Marine-Faster R-CNN algorithm is proposed in the case of complex background (e.g., sea clutter) and target characteristics. The method performs feature extraction and target recognition on PPI images generated by radar echoes with the convolutional neural network (CNN). First, to improve the accuracy of detecting marine targets and reduce the false alarm rate, Faster R-CNN was optimized as the Marine-Faster R-CNN in five respects: new backbone network, anchor size, dense target detection, data sample balance, and scale normalization. Then, JRC (Japan Radio Co., Ltd.) navigation radar was used to collect echo data under different conditions to build a marine target dataset. Finally, comparisons with the classic Faster R-CNN method and the constant false alarm rate (CFAR) algorithm proved that the proposed method is more accurate and robust, has stronger generalization ability, and can be applied to the detection of marine targets for navigation radar. Its performance was tested with datasets from different observation conditions (sea states, radar parameters, and different targets).

Effectiveness of cutting is measured by the ability to achieve material fracture with smooth knife movements. The work performed by a knife overcomes the material toughness, acts against the blade-material friction, and generates shape deformation. This paper studies how to control a 2-DOF robotic arm equipped with a force/torque sensor to cut through an object in a sequence of three moves: press, push, and slice. For each move, a separate control strategy in the Cartesian space is designed to incorporate contact and/or force constraints while following some prescribed trajectory. Experiments conducted over several types of natural foods have demonstrated smooth motions like would be commanded by a human hand.