Precise identification of dynamic models in robotics is essential to support dynamic simulations, control design, friction compensation, output torque estimation, etc. A longstanding challenge remains in the development and identification of friction models for robotic joints, given the numerous physical phenomena affecting the underlying friction dynamics which result into nonlinear characteristics and hysteresis behaviour in particular. These phenomena proof difficult to be modelled and captured accurately using physical analogies alone. This has motivated researchers to shift from physics-based to data-driven models. Currently, these methods are still limited in their ability to generalize effectively to typical industrial robot deployement, characterized by high- and low-velocity operations and frequent direction reversals. Empirical observations motivate the use of dynamic friction models but these remain particulary challenging to establish. To address the current limitations, we propose to account for unidentified dynamics in the robot joints using latent dynamic states. The friction model may then utilize both the dynamic robot state and additional information encoded in the latent state to evaluate the friction torque. We cast this stochastic and partially unsupervised identification problem as a standard probabilistic representation learning problem. In this work both the friction model and latent state dynamics are parametrized as neural networks and are integrated in the conventional lumped parameter dynamic robot model. The complete dynamics model is directly learned from the noisy encoder measurements in the robot joints. We use the Expectation-Maximisation (EM) algorithm to find a Maximum Likelihood Estimate (MLE) of the model parameters. The effectiveness of the proposed method is validated in terms of open-loop prediction accuracy in comparison with baseline methods, using the Kuka KR6 R700 as a test platform.

Non-prehensile manipulation aims to change how robots handle objects, going beyond conventional grasping, including pushing, sliding, tipping, rolling, and throwing. These manipulation modes however face new challenges in sensing, planning and control. This motivates the development of advanced and dedicated algorithms. This research explores the use of Model Predictive Control to control the motion of objects through pushing and sliding on a practical pusher-slider system. We develop a reactive path planning feedback controller that can navigate dynamic objects in real-time. To that end, we introduce a simplified model with advantageous properties. Differential flatness allows the description of dynamically feasible trajectories in a representation not subject to differential constraints, simplifying motion optimization. A temporal invariance property of the state trajectories allows us to simplify the problem further into a strictly geometric optimization where velocities are imposed in a second stage. We show that the method is sufficiently computationally efficient to support reactive replanning when facing dynamic obstacles. Experimental validation with a KUKA manipulator and vision-based tracking demonstrates the applicability of this method.