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

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5301
Author(s):  
Patricio Rivera ◽  
Edwin Valarezo Valarezo Añazco ◽  
Tae-Seong Kim
Keyword(s):  
Reinforcement Learning ◽  
Success Rate ◽  
Point Clouds ◽  
Object Manipulation ◽  
Degree Of Freedom ◽  
Robotic Hand ◽  
Robotic Hands ◽  
Flow Network ◽  
Average Success Rate ◽  
High Degree

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.

Natural object manipulation using anthropomorphic robotic hand through deep reinforcement learning and deep grasping probability network

2020 ◽  
Author(s):  
Edwin Valarezo Añazco ◽  
Patricio Rivera Lopez ◽  
Nahyeon Park ◽  
Jiheon Oh ◽  
Gahyeon Ryu ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Object Manipulation ◽  
Robotic Hand ◽  
Natural Object

scholarly journals An intelligent decision-making method for anti-jamming communication based on deep reinforcement learning

2021 ◽  
Vol 39 (3) ◽  
pp. 641-649
Author(s):  
Bailin Song ◽  
Hua Xu ◽  
Lei Jiang ◽  
Ning Rao
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Success Rate ◽  
Intelligent Decision Making ◽  
Decision Network ◽  
Intelligent Decision ◽  
Experience Replay ◽  
Average Success Rate ◽  
Convergent Algorithm ◽  
Fast Decision

In order to solve the problem of intelligent anti-jamming decision-making in battlefield communication, this paper designs an intelligent decision-making method for communication anti-jamming based on deep reinforcement learning. Introducing experience replay and dynamic epsilon mechanism based on PHC under the framework of DQN algorithm, a dynamic epsilon-DQN intelligent decision-making method is proposed. The algorithm can better select the value of epsilon according to the state of the decision network and improve the convergence speed and decision success rate. During the decision-making process, the jamming signals of all communication frequencies are detected, and the results are input into the decision-making algorithm as jamming discriminant information, so that we can effectively avoid being jammed under the condition of no prior jamming information. The experimental results show that the proposed method adapts to various communication models, has a fast decision-making speed, and the average success rate of the convergent algorithm can reach more than 95%, which has a great advantage over the existing decision-making methods.

scholarly journals Picking Towels in Point Clouds

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 713 ◽  
Author(s):  
Xiaoman Wang ◽  
Xin Jiang ◽  
Jie Zhao ◽  
Shengfan Wang ◽  
Tao Yang ◽  
...  
Keyword(s):  
Success Rate ◽  
Textile Industry ◽  
Point Clouds ◽  
Determination Method ◽  
Pose Determination ◽  
Industry Automation ◽  
Average Success Rate

Picking clothing has always been a great challenge in laundry or textile industry automation, especially when some clothes are of the same colors, material and entangled with each other. In order to solve the problem, we present a grasp pose determination method to pick towels placed in a laundry basket or on a table. In our method, it is not needed to segment towels into independent items and the target towels are not necessarily distinguishable in color. The proposed algorithm firstly segments point clouds into several convex wrinkles, and then selects the appropriate grasp point on the candidate convex wrinkle. Moreover, we plan the grasp orientation with respect to the wrinkle which can effectively reduce the grasp failure caused by the inappropriate grasp direction. We evaluate our method on picking white towels and square towels, respectively, and achieved an average success rate of about 80%.

scholarly journals Efficient Reactive Obstacle Avoidance Using Spirals for Escape

Drones ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 51
Author(s):  
Fábio Azevedo ◽  
Jaime S. Cardoso ◽  
André Ferreira ◽  
Tiago Fernandes ◽  
Miguel Moreira ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Mobile Robotics ◽  
Point Clouds ◽  
Depth Camera ◽  
Main Task ◽  
Robust Method ◽  
Aerial Vehicles ◽  
Correct Execution ◽  
Low Altitude ◽  
High Degree

The usage of unmanned aerial vehicles (UAV) has increased in recent years and new application scenarios have emerged. Some of them involve tasks that require a high degree of autonomy, leading to increasingly complex systems. In order for a robot to be autonomous, it requires appropriate perception sensors that interpret the environment and enable the correct execution of the main task of mobile robotics: navigation. In the case of UAVs, flying at low altitude greatly increases the probability of encountering obstacles, so they need a fast, simple, and robust method of collision avoidance. This work covers the problem of navigation in unknown scenarios by implementing a simple, yet robust, environment-reactive approach. The implementation is done with both CPU and GPU map representations to allow wider coverage of possible applications. This method searches for obstacles that cross a cylindrical safety volume, and selects an escape point from a spiral for avoiding the obstacle. The algorithm is able to successfully navigate in complex scenarios, using both a high and low-power computer, typically found aboard UAVs, relying only on a depth camera with a limited FOV and range. Depending on the configuration, the algorithm can process point clouds at nearly 40 Hz in Jetson Nano, while checking for threats at 10 kHz. Some preliminary tests were conducted with real-world scenarios, showing both the advantages and limitations of CPU and GPU-based methodologies.

scholarly journals Deviation Tolerance Performance Evaluation and Experiment of Picking End Effector for Famous Tea

Agriculture ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 128
Author(s):  
Yingpeng Zhu ◽  
Chuanyu Wu ◽  
Junhua Tong ◽  
Jianneng Chen ◽  
Leiying He ◽  
...  
Keyword(s):  
Success Rate ◽  
Negative Pressure ◽  
Evaluation Method ◽  
Orthogonal Experiment ◽  
Spatial Position ◽  
End Effector ◽  
Significance Level ◽  
Growth Environment ◽  
End Effectors ◽  
Average Success Rate

Accurately obtaining the posture and spatial position of tea buds through machine vision and other technologies is difficult due to the small size, different shapes, and complex growth environment of tea buds. Therefore, end effectors are prone to problems, such as picking omission and picking error. This study designs a picking end effector based on negative pressure guidance for famous tea. This end effector uses negative pressure to guide tea buds in a top-down manner, thereby correcting their posture and spatial position. Therefore, the designed end effector has deviation tolerance performance that can improve the picking success rate. The pre-experiment is designed, the tip of apical bud is referred to as the descent position, and the negative pressure range is determined to be 0.6 to 0.9 kPa. A deviation tolerance orthogonal experiment is designed. Experimental results show that various experimental factors are ranked in terms of the significance level of the effect on the average success rate, and the significance ranking is as follows: negative pressure (P) > pipe diameter (D) > descent speed (V). An evaluation method of deviation tolerance performance is presented, and the optimal experiment factor-level combination is determined as: P = 0.9 kPa, D = 34 mm, V = 20 mm/s. Within the deviation range of a 10 mm radius, the average success rate of the negative pressure guidance of the end effector is 97.36%. The designed end effector can be applied to the intelligent picking of famous tea. This study can provide a reference for the design of similar picking end effectors for famous tea.

Research on Simulation Customers Participate Type in Open Innovation

2014 ◽  
Vol 926-930 ◽  
pp. 2054-2057
Author(s):  
Jun Hui He
Keyword(s):  
Open Innovation ◽  
Degrees Of Freedom ◽  
New Products ◽  
Innovation Process ◽  
Degree Of Freedom ◽  
Three Dimensions ◽  
Dynamic Evolution ◽  
Evolution Simulation ◽  
High Degree

This paper proposed customers to participate typology based on three dimensions, which are the customers’ autonomy in the process, the nature of the firm‐customer collaboration, and the stage of the innovation process. Then proposed customers to participate in the type of open innovation framework. Through the static comparative and dynamic evolution simulation found: customers tend to be open to participate in the development of new products pre innovation, the tendency to begin to choose the low participation of degrees of freedom, and ultimately tend to opt for a high degree of freedom to participate.

Sign in / Sign up

Export Citation Format

Share Document