Experimental Setup in Robotic Study

What is the experimental setup in this study? https://arxiv.org/pdf/1808.00177.pdf

The experimental setup in the study titled "Learning Dexterous In-Hand Manipulation" is quite detailed and involves several components:

1. Shadow Dexterous Hand: This humanoid robotic hand has 24 degrees of freedom, actuated by 20 pairs of agonist-antagonist tendons.

2. 3D Tracking System: A PhaseSpace motion capture system is used to track the Cartesian position of all five finger tips. This system features active LED markers and linear detector arrays in cameras to detect positions and IDs, offering high accuracy and speeds up to 960 Hz.

3. RGB Cameras: Three Basler acA640-750uc RGB cameras with a resolution of 640x480 are placed approximately 50 cm from the Shadow hand. These are used for object pose estimation, chosen for their flexibility and low latency. This setup helps in resolving pose ambiguities which can occur with monocular vision.

4. Control: The study uses joint sensing for implementing low-level relative position control. The targets of the low-level controller are updated with relative positions given by the control policy at roughly 12 Hz.

5. Simulation: The physical system is simulated using the MuJoCo physics engine, and Unity is used for rendering images for training the vision-based pose estimator. The model of the Shadow Dexterous Hand used in the simulation is based on the one in the OpenAI Gym robotics environments but has been calibrated to match the physical system more closely.

6. Hardware Setup: The paper also notes that more details on the hardware setup are available in Appendix B of the document.

For more specific details or additional information about the experimental setup, you can refer to the paper itself, available here.

How else has the shadow dexterous hand been used in research?

The Shadow Dexterous Hand has been used in a variety of research applications, demonstrating its versatility and effectiveness in different areas:

1. Vision-Based Teleoperation: TeachNet, a neural network architecture, has been developed for vision-based teleoperation of the Shadow Dexterous Hand. This network can generate robot joint angles directly from depth images of the human hand, producing visually similar robot hand poses. TeachNet has shown superior performance compared to other vision-based teleoperation methods (Li et al.).

2. Tactile Feedback for Grasping: Equipped with BioTac tactile sensors, the Shadow Dexterous Hand has been used to develop an adaptive, compliant grasping strategy using only tactile feedback (Ganguly et al.).

3. Reinforcement Learning for Manipulation: The hand has been successfully used in reinforcement learning to train dexterous in-hand manipulation policies, with successful transfer to the physical robot (OpenAI).

4. Finger-specific Multi-agent Shadow Reward (FMSR): FMSR+IS, a method for stable manipulation with the Shadow Dexterous Hand, has been developed. It achieves a higher task success rate and better manipulation stability compared to conventional dense or sparse rewards (Tao et al.).

5. Kinematics Analysis and Simulation: Studies have focused on the kinematics analysis and simulation of the Shadow Dexterous Hand, especially on its mechanical structure characteristics and real-time control of each joint to improve dexterity during the working process (Guo Jin and Jiangnan Ni).

6. Tactile Transfer Learning: A batch tactile transfer learning technique has been explored to enable anthropomorphic robotic hands like the Shadow Hand to discriminate in-hand objects using prior tactile knowledge and a small number of training samples (Kaboli and Cheng).

7. 6D Object-in-Hand Pose Dataset: The hand has been used to propose a 6D object-in-hand pose dataset using a teleoperation method, comprising RGB-D images, proprioception, and tactile data. This method aims to address limitations of existing datasets and represent real-world scenarios (Yuyang Tu et al.).

8. Improved Sample Efficiency and Performance: Incorporating tactile information into training for simulated dexterous in-hand object manipulation tasks using the Shadow Dexterous Hand has shown to significantly improve sample efficiency and performance (Melnik et al.).

9. Torque Saturation Control Scheme: A control scheme using torque saturation to control the end-effector force of the Shadow Dexterous Hand has shown potential for grasping and dexterous manipulation activities (Nguyen and Perdereau).

10. Biologically Inspired Grasp Primitives: Development of grasp primitives for catching and lifting a sphere with the Shadow Dexterous Hand (John Lavery et al.).

These diverse applications highlight the broad potential of the Shadow Dexterous Hand in advancing robotic technology and its application in various fields.

That paper by John Lavery sounds really interesting - What are some recommended papers like it?

Here are some recommended papers related to the research by John Lavery on the Shadow Dexterous Hand:

1. "Design of Hovering Underwater Robotic Platform to Capture Harmful Marine Organisms" by Sangrok Jin et al.: This paper proposes an underwater robotic platform to eliminate harmful marine organisms like starfishes. The platform is designed with four rotatable thrusters to maintain its position and attitude during capturing manipulation. It includes extensive research on the required functions and dynamic modeling for the design (Read more).

2. "Design of Self-Tuning Gain Depth Controller for an Autonomous Underwater Vehicle with Mass Shifter Mechanism" by Mai Ba Loc and Hyeung-Sik Choi: This paper presents a self-tuning gain depth controller for the autonomous underwater vehicle KAUV-1. It uses a fuzzy logic method for depth control, addressing the need for flexible gains in different operation modes (Read more).

3. "Transferring Human Grasping Synergies to a Robot" by T. Geng, Mark H. Lee, and M. Hülse: This paper discusses a system for transferring human grasping skills to a robot. It involves extracting synergies from human grasping experiments and using them to control robot grasping, demonstrating the system's ability to grasp new objects (Read more).

4. "Dimensionality Reduction in Control and Coordination of the Human Hand" by R. Vinjamuri et al.: The paper develops a method for extracting kinematic synergies from joint-angular-velocity profiles of hand movements. This method addresses the dimensionality reduction problem in controlling and coordinating the human hand (Read more).

These papers explore various aspects of robotics and human-robot interaction, ranging from underwater applications to the transfer of human skills to robotic systems, reflecting the diverse applications and potential of robotic technologies.