A positioning controller (50) including an imaging predictive model (80) and inverse control predictive model (70). In operation, the controller (50) applies the imaging predictive model (80) to imaging data generated by an imaging device (40) to render a predicted navigated pose of the imaging device (40), and applies the control predictive model (70) to error positioning data derived from a differential aspect between a target pose of the imaging device 40) and the predicted navigated pose of the imaging device (40) to render a predicted corrective positioning motion of the imaging device (40) (or a portion of the interventional device associated with this imaging device) to the target pose. From the predictions, the controller (50) further generates continuous positioning commands controlling a corrective positioning by the interventional device (30) of the imaging device (40) (or said portion of interventional device) to the target pose based on the predicted corrective positioning motion of the interventional device (30).

A training data collection method for an interventional device (130) including a portion of an interventional device (140) and sensors (332) adapted to provide position and/or orientation and/or shape information with at least a part of the sensors being affixed to said portion of device (140). The method involves controlling one or more motion variables of the interventional device (130) in accordance with a pre-defined data point pattern, and determining, from shape data derived from said information, an estimating of a pose of said device portion (140) and an estimating of a positioning motion of the interventional device (130). The method further involves a storage of a temporal data sequence for the interventional device (130) derived from the estimated pose of the end-effector (140) for each data point, the estimated positioning motion of the interventional device (130) for each data point, and the motion variable(s) of the interventional device (130) for each data point.

A positioning controller (50) including a forward predictive model (60) and/or inverse control predictive model (70) for positioning control of an interventional device (30) including a portion (40) of an interventional device. In operation, the controller (50) may apply the forward predictive model (60) to a commanded positioning motion of the interventional device (30) to render a predicted navigated pose of the end-effector (40), and generate positioning data informative of a positioning by the interventional device (30) of said portion of interventional device (40) to a target pose based on the predicted navigated pose of said portion (40). Alternatively, antecedently or subsequently, the controller (50) may apply the control predictive model (70) to the target pose of the portion of interventional device (40) to render a predicted positioning motion of the interventional device (30), and generate positioning commands controlling a positioning by the interventional device (30) of said device portion (40) to the target pose based on the predicted positioning motion of the interventional device (30).

Techniques are disclosed that enable model predictive control of a robot based on a latent dynamics model and a reward function. In many implementations, the latent space can be divided into a deterministic portion and stochastic portion, allowing the model to be utilized in generating more likely robot trajectories. Additional or alternative implementations include many reward functions, where each reward function corresponds to a different robot task.

Techniques are disclosed that enable model predictive control of a robot based on a latent dynamics model and a reward function. In many implementations, the latent space can be divided into a deterministic portion and stochastic portion, allowing the model to be utilized in generating more likely robot trajectories. Additional or alternative implementations include many reward functions, where each reward function corresponds to a different robot task.

A positioning controller (50) including an imaging predictive model (80) and inverse control predictive model (70). In operation, the controller (50) applies the imaging predictive model (80) to imaging data generated by an imaging device (40) to render a predicted navigated pose of the imaging device (40), and applies the control predictive model (70) to error positioning data derived from a differential aspect between a target pose of the imaging device (40) and the predicted navigated pose of the imaging device (40) to render a predicted corrective positioning motion of the imaging device (40) (or a portion of the interventional device associated with this imaging device) to the target pose. From the predictions, the controller (50) further generates continuous positioning commands controlling a corrective positioning by the interventional device (30) of the imaging device (40) (or said portion of interventional device) to the target pose based on the predicted corrective positioning motion of the interventional device (30).