To facilitate a systematic comparison of AI-based force control techniques, a common evaluation framework should be established that encompasses several key performance metrics. These metrics could include:
Success Rate: The percentage of successful task completions, such as successful peg-in-hole insertions or effective deburring operations, providing a direct measure of effectiveness.
Force Tracking Accuracy: Quantifying the deviation between the desired and actual forces exerted during tasks, which is crucial for maintaining quality in force-controlled applications.
Execution Time: Measuring the time taken to complete tasks, which is essential for assessing efficiency in manufacturing processes.
Safety Metrics: Evaluating the safety of operations, particularly in contact-rich tasks, by monitoring the forces exerted and ensuring they remain within safe thresholds.
Robustness to Variability: Assessing how well the control strategies perform under varying conditions, such as changes in material properties or environmental factors.
By standardizing these metrics, researchers can create a benchmark dataset that includes diverse scenarios and conditions reflective of real-world applications. This dataset would enable comparative studies across different AI-driven force control methodologies, fostering collaboration and knowledge sharing within the research community. Furthermore, integrating simulation environments like MuJoCo or IsaacGym can enhance the realism of evaluations, bridging the gap between simulated and real-world performance, and ultimately driving advancements in AI applications for manufacturing.