Online Learning of Heterogeneous Locomotion Dynamics under Reinforcement Learning Policies for Precise Loco-Manipulation
Overview
This research addresses the challenge of reusing pretrained locomotion policies for precise loco-manipulation by introducing an online method to learn closed-loop dynamics under frozen policies. The authors propose a contaminated Gaussian model combined with Gaussian processes and random Fourier features to approximate heterogeneous responses caused by unobserved latent conditions. By weighting online updates based on the probability of predictable responses, the method reduces bias and fluctuations in dynamics estimates. Model predictive control (MPC) is then used for accurate floating-base motion prediction. The study establishes a finite-sample error bound ensuring uniform convergence and demonstrates consistent end-effector tracking with errors of 0.01 m across various manipulator configurations and tasks through both simulation and hardware experiments.
Generated from attributed reports · Updated 1 hours ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · end-effector tracking error: 0.01 other · Basis not reported
“h errors of 0.01 m.”
Exact source · revision 1Source owner not reported
Reported quantity · Gaussian components: 2 other · Basis not reported
“meaning that the same observed state will lead to different motions. Direct learning without accounting for this heterogeneity introduces bias and fluctuations into dynamics estimates, degrading floating-base prediction and end-effector tracking. To address this problem, a contaminated Gaussian model is proposed to approximate the effects of latent conditions. The model uses two Gaussian components to approximate pre”
Exact source · revision 1Source owner not reported
Report timeline
Follow attributed reports and material updates.
- arXiv Robotics — research abstractsSignalOnline Learning of Heterogeneous Locomotion Dynamics under Reinforcement Learning Policies for Precise Loco-Manipulation
This research addresses the challenge of reusing pretrained locomotion policies for precise loco-manipulation by introducing an online method to learn closed-loop dynamics under frozen policies. The authors propose a contaminated Gaussian model combined with Gaussian processes and random Fourier features to approximate heterogeneous responses caused by unobserved latent conditions. By weighting online updates based on the probability of predictable responses, the method reduces bias and fluctuations in dynamics estimates. Model predictive control (MPC) is then used for accurate floating-base motion prediction. The study establishes a finite-sample error bound ensuring uniform convergence and demonstrates consistent end-effector tracking with errors of 0.01 m across various manipulator configurations and tasks through both simulation and hardware experiments.
Event coverage history
There is not enough continuous observation data to show a trend.
Timezone · UTC
Article dates follow your selected timezone. Briefing editions use Hong Kong time (UTC+8).