The seafood processing industry provides fertile ground for robotics to impact the future-of-work from multiple perspectives including productivity, worker safety, and quality of work life. The robotics research challenge is the realization of flexible and reliable manipulation of soft, deformable, slippery, spiky and scaly objects. In this paper, we propose a novel robot end effector, called HASHI, that employs chopstick-like appendages for precise and dexterous manipulation. This gripper is capable of in-hand manipulation by rotating its two constituent sticks relative to each other and offers control of objects in all three axes of rotation by imitating human use of chopsticks. HASHI delicately positions and orients food through embedded 6-axis force-torque sensors. We derive and validate the kinematic model for HASHI, as well as demonstrate grip force and torque readings from the sensorization of each chopstick. We also evaluate the versatility of HASHI through grasping trials of a variety of real and simulated food items with varying geometry, weight, and firmness.

Motion capture through tracking retroreflectors obtains highly accurate pose estimation, which is frequently used in robotics. Unlike commercial motion capture systems, fiducial marker-based tracking methods, such as AprilTags, can perform relative localization without requiring a static camera setup. However, popular pose estimation methods based on fiducial markers have lower localization accuracy than commercial motion capture systems. We propose Mobile MoCap, a system that utilizes inexpensive near-infrared cameras for accurate relative localization even while in motion. We present a retroreflector feature detector that performs 6-DoF (six degrees-of-freedom) tracking and operates with minimal camera exposure times to reduce motion blur. To evaluate the proposed localization technique while in motion, we mount our Mobile MoCap system, as well as an RGB camera to benchmark against fiducial markers, onto a precision-controlled linear rail and servo. The fiducial marker approach employs AprilTags, which are pervasively used for localization in robotics. We evaluate the two systems at varying distances, marker viewing angles, and relative velocities. Across all experimental conditions, our stereo-based Mobile MoCap system obtains higher position and orientation accuracy than the fiducial approach. The code for Mobile MoCap is implemented in ROS 2 and made publicly available at https://github.com/RIVeR-Lab/mobile_mocap.