Leveraging Simulation-Based Model Preconditions for Fast Action Parameter Optimization with Multiple Models

Optimizing robotic action parameters is a significant challenge for manipulation tasks that demand high levels of precision and generalization. Using a model-based approach, the robot must quickly reason about the outcomes of different actions using a predictive model to find a set of parameters that will have the desired effect. The model may need to capture the behaviors of rigid and deformable objects, as well as objects of various shapes and sizes. Predictive models often need to trade-off speed for prediction accuracy and generalization. This paper proposes a framework that leverages the strengths of multiple predictive models, including analytical, learned, and simulation-based models, to enhance the efficiency and accuracy of action parameter optimization. Our approach uses Model Deviation Estimators (MDEs) to determine the most suitable predictive model for any given state-action parameters, allowing the robot to select models to make fast and precise predictions. We extend the MDE framework by not only learning sim-to-real MDEs, but also sim-to-sim MDEs. Our experiments show that these sim-to-sim MDEs provide significantly faster parameter optimization as well as a basis for efficiently learning sim-to-real MDEs through finetuning. The ease of collecting sim-to-sim training data also allows the robot to learn MDEs based directly on visual inputs and local material properties.

Authors

• M. Yunus Seker
• Oliver Kroemer

Keywords

• Deformable models
• Visualization
• Accuracy
• Shape
• Training data
• Predictive models
• Robots
• Optimization
• Intelligent robots
• Material properties
• Preconditioning
• Prediction Model
• Prediction Accuracy
• Learning Models
• Model Selection
• Rigid Body
• Model-based Approach
• Manipulation Tasks
• Deformable Objects
• Multiple Prediction Models
• Accuracy Of Model
• Fine-tuned
• Simulation Model
• Optimization Process
• Set Of Models
• Focus Of This Paper
• Real-world Data
• Real-world Datasets
• Target Object
• Height Map
• Optimal Action
• Real-world Observations
• Task Planning
• Target Scene
• Actual Accuracy
• Fine-tuning Process
• Heuristic Model
• Reward Function
• Robotic Applications