A probabilistic data-driven model for planar pushing
This paper presents a data-driven approach to model planar pushing interaction to predict both the most likely outcome of a push and its expected variability. The learned models rely on a variation of Gaussian processes with input-dependent noise called Variational Heteroscedastic Gaussian processes (VHGP) that capture the mean and variance of a stochastic function. We show that we can learn accurate models that outperform analytical models after less than 100 samples and saturate in performance with less than 1000 samples. We validate the results against a collected dataset of repeated trajectories, and use the learned models to study questions such as the nature of the variability in pushing, and the validity of the quasi-static assumption.
Code (0)
등록된 구현이 없습니다.
Tasks
Gaussian ProcessesmodelSimilar Papers 제목 키워드 기반
Online Learning in Planar Pushing with Combined Prediction Model
Pushing is a useful robotic capability for positioning and reorienting objects. The ability to accurately predict the effect of pushes can enable efficient trajectory planning and complicated object manipulation. Physica…
PredictionTrajectory PlanningHow Physics and Background Attributes Impact Video Transformers in Robotic Manipulation: A Case Study on Planar Pushing
As model and dataset sizes continue to scale in robot learning, the need to understand how the composition and properties of a dataset affect model performance becomes increasingly urgent to ensure cost-effective data co…
FrictionObjectTrajectory PredictionModel-Based Adaptive Precision Control for Tabletop Planar Pushing Under Uncertain Dynamics
Data-driven planar pushing methods have recently gained attention as they reduce manual engineering effort and improve generalization compared to analytical approaches. However, most prior work targets narrow capabilitie…
Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncing
An efficient, generalizable physical simulator with universal uncertainty estimates has wide applications in robot state estimation, planning, and control. In this paper, we build such a simulator for two scenarios, plan…
Gaussian ProcessesObjectState EstimationA Data-Efficient Approach to Precise and Controlled Pushing
Decades of research in control theory have shown that simple controllers, when provided with timely feedback, can control complex systems. Pushing is an example of a complex mechanical system that is difficult to model a…
FrictionModel Predictive Control