The advancement in robotic grasping and manipulation has elicited an increased research interest in the development of household robots capable of performing a plethora of complex tasks. These advancements require the shift of robotics research from a laboratory setting to dynamic and unstructured home environments. In this work, we focus on a comprehensive data collection and analysis of key attributes involved in the selection of grasping and manipulation strategies for the successful execution of kitchen tasks. An unprecedented dataset that comprises over 7 hours of high-definition videos that were analyzed to classify more than 10,000 kitchen activities annotated with 24 attributes each has been created. Machine learning techniques were employed to automate the annotation process partially by extracting grasp types, hand, and object information from the videos. The annotated dataset was analyzed using clustering algorithms to identify underlying patterns. This study also identifies key attributes and specific data that require focus during data collection based on inter-subject variability. The insights from this study can be used to improve the speed, quality, and effectiveness of data collection. It also helps identify the strategies employed by the humans for the execution of kitchen tasks and transfer the necessary skills to a robotic end-effector enabling it to complete the tasks autonomously or collaborate with humans.

The advent of collaborative industrial and house-hold robotics has blurred the demarcation between the human and robot workspace. The capability of robots to function efficiently alongside humans requires new research to be conducted in dynamic environments as opposed to the traditional well-structured laboratory. In this work, we propose an efficient skill transfer methodology comprising intuitive interfaces, efficient optical tracking systems, and compliant control of robotic arm-hand systems. The lightweight wearable interfaces mounted with robotic grippers and hands allow the execution of dexterous activities in dynamic environments without restricting human dexterity. The fiducial and reflective markers mounted on the interfaces facilitate the extraction of positional and rotational information allowing efficient trajectory tracking. As the tasks are performed using the mounted grippers and hands, gripper state information can be directly transferred. The hardware-agnostic nature and efficiency of the proposed interfaces and skill transfer methodology are demonstrated through the execution of complex tasks that require increased dexterity, writing and drawing.

Design optimization can lead to the development of robotic end-effectors with optimal grasping and dexterous, in-hand manipulation capabilities. In particular, the finger link dimensions have been identified as one of the primary design parameters that affects the performance of a robotic gripper. The ability of a gripper to manipulate objects is mainly attributed to the interaction between a set of coordinated fingers. This coordination is primarily affected by the inter-finger distance. This paper presents a framework for finding an appropriate distance between the finger bases of a two-fingered robotic gripper so as to increase the dexterous manipulation workspace for a range of object sizes. To do that, a parallel multi-start search algorithm is employed to solve a multiparametric optimization problem. The results demonstrate that different distances lead to completely different workspace shapes and that the ratio defined by the area of the optimized workspace (nominator) and the union of all workspaces (denominator) is always significantly less than 1. This means that the area of the union of all workspaces is always larger than the area of the "optimized" workspace. Based on these results a multi-modal robotic gripper with movable finger bases was developed. The proposed gripper can vary the distance between the finger bases online and it offers an increased dexterous manipulation workspace without sacrificing grasping performance.

The concept of teleoperation has been studied since the advent of robotics and has found use in a wide range of applications, including exploration of remote or dangerous environments (e.g., space missions, disaster management), telepresence based time optimisation (e.g., remote surgery) and robot learning. While a significant amount of research has been invested into the field, intricate manipulation tasks still remain challenging from the user perspective due to control complexity. In this paper, we propose an intuitive, affordances oriented telemanipulation framework for a dual robot arm hand system. An object recognition module is utilised to extract scene information and provide grasping and manipulation assistance to the user, simplifying the control of adaptive, multi-fingered hands through a commercial Virtual Reality (VR) interface. The system's performance was experimentally validated in a remote operation setting, where the user successfully performed a set of bimanual manipulation tasks.

Adaptive robot hands are typically created by introducing structural compliance either in their joints (e.g., implementation of flexure joints) or in their finger-pads. In this paper, we present a series of alternative uses of structural compliance for the development of simple, adaptive, compliant and/or under-actuated robot grippers and hands that can efficiently and robustly execute a variety of grasping and dexterous, in-hand manipulation tasks. The proposed designs utilize only one actuator per finger to control multiple degrees of freedom and they retain the superior grasping capabilities of the adaptive grasping mechanisms even under significant object pose or other environmental uncertainties. More specifically, in this work, we introduce, discuss, and evaluate: a) the concept of compliance adjustable motions that can be predetermined by tuning the in-series compliance of the tendon routing system and by appropriately selecting the imposed tendon loads, b) a design paradigm of pre-shaped, compliant robot fingers that adapt / conform to the object geometry and, c) a hyper-adaptive finger-pad design that maximizes the area of the contact patches between the hand and the object, maximizing also grasp stability. The proposed hands use mechanical adaptability to facilitate and simplify the efficient execution of robust grasping and dexterous, in-hand manipulation tasks by design.