Data-Driven Contact Clustering for Robot Simulation

We propose a novel data-driven learning-based contact clustering (i. e. , of contact points and contact normals) framework for rigid-body robot simulation, with its accuracy established/verified by real experimental data. We first construct an experimental robotic setup with force/torque (F/T) sensors to collect real contact motion/force data. We then design a multilayer perceptron (MLP) network for the contact clustering based on the full motion and force/torque information of the contacts. We also adopt the constraint-based optimization contact solver to facilitate the learning of our MLP network during the training. Our proposed data-driven/learning-based contact clustering framework is then verified against the experimental setup, compared with other techniques/simulators and shown to significantly (or meaningfully) enhance the accuracy of contact simulation as compared to them.

Authors

• Myungsin Kim
• Jaemin Yoon
• Dongwon Son
• Dongjun Lee

Keywords

• Force
• Numerical models
• Robot sensing systems
• Data models
• Numerical stability
• Optimization
• Simulated Robot
• Experimental Setup
• Contact Point
• Multilayer Perceptron
• Simulation Accuracy
• Multilayer Perceptron Network
• Perceptron Network
• Clustering Framework
• K-means
• Experiment Data
• Contact Surface
• Rigid Body
• First Contact
• Normal Direction
• Contact Force
• Real Robot
• Contact Data
• Physics Engine
• Normal Contact
• External Moment
• Number Of Contact Points
• Warm Start
• Optimal Contact
• Learning-based Framework
• Seven-dimensional
• Edge Contact
• Coulomb Friction
• Friction Coefficient
• Angular Velocity