Data-Driven Precision Control of Soft Helicoid Robots
Overview
This paper extends a data-driven control methodology grounded in adiabatic spectral submanifolds (aSSMs) from simulation to hardware. The authors learn a five-dimensional aSSM-reduced nonlinear ordinary differential equation (ODE) that accurately models the dynamics of a soft-helicoid trunk robot. Using this ODE within a model predictive control scheme, they demonstrate real-time precision control across various challenging closed-loop trajectory-tracking tasks, ranging from 2D to 3D trajectories with static and dynamic constraints. The study finds that their methodology enhances accuracy and precision compared to available data-driven linear methods and model-free reinforcement learning. While focused on a soft-helicoid trunk robot, the approach relies solely on hardware data and is applicable to a broader class of soft robotic systems.
Generated from attributed reports · Updated 8 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.
No current evidence-backed claims. Missing information remains not reported.
Report timeline
Follow attributed reports and material updates.
- arXiv Robotics — research abstractsSignalData-Driven Precision Control of Soft Helicoid Robots
This paper extends a data-driven control methodology grounded in adiabatic spectral submanifolds (aSSMs) from simulation to hardware. The authors learn a five-dimensional aSSM-reduced nonlinear ordinary differential equation (ODE) that accurately models the dynamics of a soft-helicoid trunk robot. Using this ODE within a model predictive control scheme, they demonstrate real-time precision control across various challenging closed-loop trajectory-tracking tasks, ranging from 2D to 3D trajectories with static and dynamic constraints. The study finds that their methodology enhances accuracy and precision compared to available data-driven linear methods and model-free reinforcement learning. While focused on a soft-helicoid trunk robot, the approach relies solely on hardware data and is applicable to a broader class of soft robotic systems.
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).