What basic concept of robotics will be most difficult for humans to compete with??? Deep Deterministic Policy Gradient - DDPG). This approach is powerful for tasks where the desired behavior is hard to define explicitly but easy to evaluate. Robots can have AI available to run alternative scenarios virtually in milliseconds that can be implemented on the next operative cycle or in some instances ahead of the first operative cycle.

Making a robotic arm trainable by feedback involves allowing the robot to learn or adjust its movements based on sensory input or external guidance. Here are several approaches to achieve this:

1. Teach Pendant/Manual Guidance:

• How it works: A human operator physically guides the robot arm through the desired trajectory. The robot's encoders record the joint angles at various points, and this data is stored as a program.
• Feedback: The "feedback" here is the direct manipulation by the human. The robot learns the path from this kinesthetic feedback.
• Implementation: Many industrial robots have teach pendants for this purpose. For smaller robots, you might implement a "free drive" mode where the motors are de-energized or compliant, allowing easy manual movement. Sensors (like force/torque sensors at the wrist) can also enhance this by allowing the robot to respond to the operator's touch.

2. Learning from Demonstration (LfD) / Programming by Demonstration (PbD):

• How it works: Similar to manual guidance, but the robot might use more sophisticated methods to generalize the demonstrated trajectory. This could involve:
  • Kinesthetic Teaching: As described above.
  • Teleoperation: A human controls the robot remotely, and the robot records the commands and resulting movements.
  • Vision-based Demonstration: The robot observes a human performing a task and tries to replicate it.
• Feedback: The feedback comes from the human demonstration. The robot learns the task from observing or being guided.
• Implementation: This often involves algorithms that can learn motion primitives, generalize paths to new starting points, or adapt to variations. Libraries and frameworks like ROS (Robot Operating System) have packages that support LfD.

3. Reinforcement Learning (RL):

• How it works: The robot learns through trial and error by interacting with its environment. It receives a reward signal based on the outcome of its actions. The robot's control policy is adjusted to maximize the cumulative reward over time.
• Feedback: The reward signal acts as the feedback, indicating how well the robot is performing the task.
• Implementation: This requires defining a reward function that guides the learning process. It also involves choosing an appropriate RL algorithm (e.g., Q-learning, Deep Deterministic Policy Gradient - DDPG). This approach is powerful for tasks where the desired behavior is hard to define explicitly but easy to evaluate.

4. Error-Based Learning (e.g., using sensors):

• How it works: The robot uses sensors (e.g., cameras, force/torque sensors, proximity sensors) to perceive the environment and correct its actions based on the error between the desired state and the current state.
• Feedback: The sensor readings provide the feedback about the robot's performance and the state of the environment.
• Implementation: This often involves control loops that use sensor data to adjust the robot's movements. For example, visual servoing uses camera feedback to guide the robot to a desired pose relative to an object. Force control uses force/torque sensor feedback to enable the robot to perform tasks involving contact with the environment.

Which method is most suitable depends on the specific task and the type of feedback available.

To implement feedback-based training on your robotic arm, you would typically need:

1. Sensors: To provide the necessary feedback (e.g., encoders for joint angles, cameras for vision, force/torque sensors).
2. A Control System: To process the feedback and adjust the robot's actions. This could be a microcontroller, a more powerful computer, or the robot's dedicated controller.
3. Learning Algorithms (if applicable): For LfD or RL approaches.

Could you describe the type of task you want your robotic arm to learn? Knowing this will help in suggesting a more specific approach.

Here is a list of some manufacturers of force torque sensors:

• ATI Industrial Automation: Offers a wide range of multi-axis force/torque sensors used in robotics, testing, and automation.
• AMTI (Advanced Mechanical Technology, Inc.): Specializes in six-axis force and torque sensors for research and industrial applications, including biomechanics and robotics.
• Schunk: Provides industrial force/torque sensors designed to give robots sensitivity for precise manufacturing processes.
• FUTEK Advanced Sensor Technology: Manufactures multi-axis force and torque sensors, including bi-axial and tri-axial load cells, for various applications.
• Interface: Offers multi-axis sensors as well as axial torsion load cells and other force measurement solutions.
• HBM (Hottinger Brüel & Kjær): Produces multi-component sensors capable of measuring forces and torques in multiple directions with high precision.
• Kistler: Offers piezoelectric multi-component force sensors for measuring forces and torques along orthogonal axes.
• JR3, Inc.: Specializes in multi-axis force/torque sensors, often used in robotics.
• Althen Sensors & Controls: Provides multi-axis force sensors designed for simultaneous measurement of forces along multiple axes.
• Changzhou Kunwei Sensing Technology Co., Ltd.: Offers multi-axis force torque sensors for intricate force and torque evaluations.
• Strainsert Company: While mentioned as a torque sensor manufacturer, they also produce load cells which can be part of force measurement systems.

This list is not exhaustive, but it includes several prominent manufacturers in the field. The best choice for you will depend on your specific application requirements, such as the number of axes you need to measure, the force and torque ranges, the required accuracy, and the environmental conditions.