Difficulty in optimizing robot performance due to complex configuration parameters and lack of benchmarking tools.
Difficulty in calibrating robotic systems for accurate data collection and policy learning.
Robotic adaptation suffers from a data collection bottleneck, hindering reliable policy deployment.
The current robot design process is inefficient and relies heavily on assumptions that may not hold true across different scenarios.
Difficulty in running rigorous evaluations for robotics policies due to the high number of rollouts required.
Teams struggle with auditing data quality for robotics datasets, leading to inefficient use of resources.
Robotics teams struggle with quality control and reproducibility of training datasets due to fragmented processing pipelines.