Hand-arm autonomous grasping: synergistic motions to enhance the learning process

Automatic Temperature Parameter Tuning for Reinforcement Learning Using Path Integral Policy Improvement

In this article, we propose a novel variant of path integral policy improvement with covariance matrix adaptation (PI2-CMA), which is a reinforcement learning (RL) algorithm that aims to optimize a parameterized policy for the continuous behavior of robots. PI2-CMA has a hyperparameter called the temperature parameter, and its value is critical for performance; however, little research has been conducted on it and the existing method still contains a tunable parameter, which can be critical to performance. Therefore, tuning by trial and error is necessary in the existing method. Moreover, we show that there is a problem setting that cannot be learned by the existing method. The proposed method solves both problems by automatically adjusting the temperature parameter for each update. We confirmed the effectiveness of the proposed method using numerical tests.

Validations of various in-hand object manipulation strategies employing a novel tactile sensor developed for an under-actuated robot hand

Prisma Hand II is an under-actuated prosthetic hand developed at the University of Naples, Federico II to study in-hand manipulations during grasping activities. 3 motors equipped on the robotic hand drive 19 joints using elastic tendons. The operations of the hand are achieved by combining tactile hand sensing with under-actuation capabilities. The hand has the potential to be employed in both industrial and prosthetic applications due to its dexterous motion capabilities. However, currently there are no commercially available tactile sensors with compatible dimensions suitable for the prosthetic hand. Hence, in this work, we develop a novel tactile sensor designed based on an opto-electronic technology for the Prisma Hand II. The optimised dimensions of the proposed sensor made it possible to be integrated with the fingertips of the prosthetic hand. The output voltage obtained from the novel tactile sensor is used to determine optimum grasping forces and torques during in-hand manipulation tasks employing Neural Networks (NNs). The grasping force values obtained using a Convolutional Neural Network (CNN) and an Artificial Neural Network (ANN) are compared based on Mean Square Error (MSE) values to find out a better training network for the tasks. The tactile sensing capabilities of the proposed novel sensing method are presented and compared in simulation studies and experimental validations using various hand manipulation tasks. The developed tactile sensor is found to be showcasing a better performance compared to previous version of the sensor used in the hand.

Vision-Based Robotic Arm Control Algorithm Using Deep Reinforcement Learning for Autonomous Objects Grasping

While working side-by-side, humans and robots complete each other nowadays, and we may say that they work hand in hand. This study aims to evolve the grasping task by reaching the intended object based on deep reinforcement learning. Thereby, in this paper, we propose a deep deterministic policy gradient approach that can be applied to a numerous-degrees-of-freedom robotic arm towards autonomous objects grasping according to their classification and a given task. In this study, this approach is realized by a five-degrees-of-freedom robotic arm that reaches the targeted object using the inverse kinematics method. You Only Look Once v5 is employed for object detection, and backward projection is used to detect the three-dimensional position of the target. After computing the angles of the joints at the detected position by inverse kinematics, the robot’s arm is moved towards the target object’s emplacement thanks to the algorithm. Our approach provides a neural inverse kinematics solution that increases overall performance, and its simulation results reveal its advantages compared to the traditional one. The robot’s end grip joint can reach the targeted location by calculating the angle of every joint with an acceptable range of error. However, the accuracy of the angle and the posture are satisfied. Experiments reveal the performance of our proposal compared to the state-of-the-art approaches in vision-based grasp tasks. This is a new approach to grasp an object by referring to inverse kinematics. This method is not only easier than the standard one but is also more meaningful for multi-degrees of freedom robots.

Object Manipulation with an Anthropomorphic Robotic Hand via Deep Reinforcement Learning with a Synergy Space of Natural Hand Poses

Anthropomorphic robotic hands are designed to attain dexterous movements and flexibility much like human hands. Achieving human-like object manipulation remains a challenge especially due to the control complexity of the anthropomorphic robotic hand with a high degree of freedom. In this work, we propose a deep reinforcement learning (DRL) to train a policy using a synergy space for generating natural grasping and relocation of variously shaped objects using an anthropomorphic robotic hand. A synergy space is created using a continuous normalizing flow network with point clouds of haptic areas, representing natural hand poses obtained from human grasping demonstrations. The DRL policy accesses the synergistic representation and derives natural hand poses through a deep regressor for object grasping and relocation tasks. Our proposed synergy-based DRL achieves an average success rate of 88.38% for the object manipulation tasks, while the standard DRL without synergy space only achieves 50.66%. Qualitative results show the proposed synergy-based DRL policy produces human-like finger placements over the surface of each object including apple, banana, flashlight, camera, lightbulb, and hammer.

The State of Lifelong Learning in Service Robots:

Service robots are appearing more and more in our daily life. The development of service robots combines multiple fields of research, from object perception to object manipulation. The state-of-the-art continues to improve to make a proper coupling between object perception and manipulation. This coupling is necessary for service robots not only to perform various tasks in a reasonable amount of time but also to continually adapt to new environments and safely interact with non-expert human users. Nowadays, robots are able to recognize various objects, and quickly plan a collision-free trajectory to grasp a target object in predefined settings. Besides, in most of the cases, there is a reliance on large amounts of training data. Therefore, the knowledge of such robots is fixed after the training phase, and any changes in the environment require complicated, time-consuming, and expensive robot re-programming by human experts. Therefore, these approaches are still too rigid for real-life applications in unstructured environments, where a significant portion of the environment is unknown and cannot be directly sensed or controlled. In such environments, no matter how extensive the training data used for batch learning, a robot will always face new objects. Therefore, apart from batch learning, the robot should be able to continually learn about new object categories and grasp affordances from very few training examples on-site. Moreover, apart from robot self-learning, non-expert users could interactively guide the process of experience acquisition by teaching new concepts, or by correcting insufficient or erroneous concepts. In this way, the robot will constantly learn how to help humans in everyday tasks by gaining more and more experiences without the need for re-programming. In this paper, we review a set of previously published works and discuss advances in service robots from object perception to complex object manipulation and shed light on the current challenges and bottlenecks.

Analysis of hand-forearm anthropometric components in assessing handgrip and pinch strengths of school-aged children and adolescents: a partial least squares (PLS) approach

Background

The purpose of this study was to examine the influence of hand-forearm anthropometric dimensions on handgrip and pinch strengths among 7–18 years children and adolescents and to investigate the extent to which these variables can be used to predict hand strength.

Methods

Four types of hand strengths including handgrip, tip to tip, key, and three-jaw chuck pinches were measured in 2637 healthy children and adolescents (1391 boys and 1246 girls) aged 7–18 years using standard adjustable Jamar hydraulic hand dynamometer and pinch gauge. A set of 17 hand-forearm anthropometric dimensions were also measured with an accurate digital caliper and tape measure.

Results

No significant differences were found between the hand strengths of boys and girls up to the age of 10 years. Gender related differences in handgrip and pinches were observed from the age of 11 years onwards, with boys always being stronger. The dominant hand was stronger than the non-dominant hand (8% for handgrip and by about 10% for all three types of pinches). The strongest correlations were found between the hand length and hand strengths (r > 0.83 for handgrip and three all pinches; p < 0.001, 2-tailed). Based on the partial least squares (PLS) analysis, 8 out of 17 anthropometric indices including hand length, hand circumference, thumb length, index finger length, middle finger length, and forearm length had considerable loadings in the PLS analysis, which together accounted for 46% of the total variance.

Conclusions

These results may be used by health professionals in clinical settings as well as by designers to create ergonomic hand tools.