This work introduces the New Dexterity modular, dexterous, anthropomorphic, open-source, bimanual manipulation platform (OpenBMP) that is designed for research and rapid experimentation in robot grasping, dexterous manipulation, and bimanual manipulation. The platform combines adaptive and hybrid actuation systems with lockable joints, facilitating transitions between the execution of delicate and forceful tasks. Antagonistic tendon-driven elbows and inline actuator transmissions reduce the system’s inertial mass while enhancing energy efficiency and overall performance. Leveraging 3D printing and carbon fiber reinforced manufacturing of core parts, the platform is easy to replicate and highly modular. This paper presents the details of the design, the actuation principles, and the experimental validation of the efficiency of the platform with the execution of complex teleoperation and telemanipulation tasks. The designs, the electronics, and the code are open-sourced to allow replication by others.

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.

Telemanipulation is widely used in robotics applications, ranging from maintenance of various industrial systems to search and rescue response in remote and/or hazardous environments. Human operators are often responsible for the control of such robotic systems. However, these remote interactions require highly trained and experienced operators owing to their complex nature. Semi-autonomous systems are presented as an alternative to complex and counter-intuitive manual systems, combining decoded user intentions with autonomous control modules. This paper proposes a semi-autonomous framework for robotic telemanipulation that employs Electromyography (EMG) based motion decoding and potential fields to execute complex object stacking tasks with a dexterous robot arm-hand system. Even though simple EMG-based teleoperation is promising, the signals are often noisy leading to induced randomness and control errors. To assist the user during task executions, potential fields are utilized to avoid obstacles and guide the robot end-effector toward the objects of interest, thus reducing the cognitive load on the user and the need for accurate predictions. The user's motion is decoded from the myoelectric activations of the human upper arm and upper torso using a Random Forest-based regression methodology. The objects are detected in the environment with an external camera that provides their goal poses to the potential fields scheme. EMG control and potential fields work in a synergistic manner simplifying the system's operation. The framework performance is experimentally validated in real-time experiments involving complex cube and cylinder stacking tasks.

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.

A popular solution for connecting different components in modern electronics, such as mobile phones, laptops, tablets, etc, is the use of flexible flat cables (FFC). Typically, it takes hours of repetition from a highly trained worker, or a high precision autonomous robot with specialised end effectors to reliably manage the installation of these cables. Human workers are prone to error, and cannot work endlessly without a break, while the robots often come with a significant expense, and require a substantial amount of time to program and reprogram. Additionally, the use of sophisticated sensing elements further increases the complexity of the required control system. As a result, the performance and robustness of such systems is far from sufficient, hindering their mass adoption. The manipulation of FFCs is also quite challenging. In this work, we focus on the robotic manipulation of a plethora of flexible cables, proposing a multi-modal gripper with locally-dexterous tips and active fingernails. The fingers of the gripper are equipped with: i) locally-dexterous fingertips that accommodate manipulation-capable degrees of freedom, ii) a combination of Nitinol-based active fingernails and suction cups that allow picking up and handling of cables that rest on flat surfaces, and iii) compliant finger-pads that conform to the object surface to increase grasping stability. The proposed robotic gripper is equipped with a camera and a perception system that allow for the execution of complex cable manipulation and assembly tasks in dynamic environments.

Grasping and manipulation are complex and demanding tasks, especially when executed in dynamic and unstructured environments. Typically, such tasks are executed by rigid articulated end-effectors, with a plethora of actuators that need sophisticated sensing and complex control laws to execute them efficiently. Soft robotics offers an alternative that allows for simplified execution of these demanding tasks, enabling the creation of robust, efficient, lightweight, and affordable solutions that are easy to control and operate. In this work, we introduce a new class of soft, kirigami-based robotic grippers, we study their post-contact behavior, and we investigate different cut patterns for their development. We follow an experimental approach in which several designs are proposed and employed in a series of grasping and force exertion tests to compare their capabilities and post-contact behavior. The results of such experiments indicate a clear relationship between degree of reconfiguration and grasping force, and provide key insights into the effect of the cut patterns in the performance of the designs. These findings are then used in the design process of an improved version of multi-layer, disposable kirigami grippers that are fabricated employing simple 3D printed layers and silicone rubber using the concept of Hybrid Deposition Manufacturing (HDM). A series of experimental results demonstrate that the proposed design and manufacturing methods can enable the creation of soft, kirigami-based grippers with superior grasping capabilities that can handle delicate, contaminated, and everyday life objects and can even be disposed off in an automated way (e.g., after handling hazardous materials, such as medical waste).

To communicate, the ~ 1.5 million Americans living with deafblindess use tactile American Sign Language (t-ASL). To provide Deafßilind (DB) individuals with a means of using their primary communication language without the use of an interpreter, we developed an assistive technology that promotes their autonomy. The TATUM (Tactile ASL Translational User Mechanism) anthropomorphic arm hand system leverages previous developments of a fingerspelling hand to sign more complex ASL words and phrases. The TATUM hand-wrist system is attached onto a 4 DOF robot arm and a human motion recognition and human to robot gesture transfer framework is used for signing recognition and replication. In particular, signing trajectories based on vision-based motion capture data from a sign demonstrator were used to control the robot's actuators. The performance of the system was evaluated through tactile based sign recognition performed by a blinded user and for its accuracy with novice, sighted users.