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.

The increasing complexity of tasks performed by hybrid aerial robotic systems, such as tail-sitters, demands a more integrated approach to their design. Traditional sequential design methods fall short because they separate the control system design from the conceptual design, limiting the poten-tial for discovering coupled solutions. This disjointed process constrains the design space, making it difficult to optimize both the control performance and system dynamics simultaneously. In response to this limitation, there has been growing interest in mission-specific dynamic design procedures, which aim to address specific operational challenges by integrating control and design early in the development process. The multi-disciplinary approach of control co-design (CCD) expands the design space by solving control and system design problems concurrently. The recently introduced DAIMYO framework demonstrated that combining multi-fidelity modelling with a nested CCD approach can tackle the situ-to-reel gap. However, DAIMYO's reliance on Bayesian optimization to account for the computational cost increase of a nested formulation limits its scalability. To address these issues, we propose KUGE, a simultaneous CCD strategy that reduces computational complexity and overcomes dimensionality restrictions through a combined effort of stochastic optimization and Gaussian processes. We validate the effectiveness of KUGE by applying it to the dynamic design of a tail-sitter, showing that it is competitive with the DAIMYO architecture while offering greater computational efficiency.

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.

There is an emerging industrial demand for predictive maintenance algorithms that exhibit high levels of predictive accuracy. Such condition monitoring tools must estimate dynamic quantities, such as Remaining Useful Lifetime (RUL) and the State of Health (SOH), based on a, typically, restricted set of measurements that can be obtained in an operational setting. These quantities exhibit inherent stochasticity and can only be approximately determined a posteriori to system failure. This paper proposes a generic prognostic tool for probabilistic condition monitoring of mechatronic systems, with the aim to improve the probabilistic prediction of condition metrics, specifically RUL and SOH. Therefore we propose to identify a Hidden Markov Model (HMM) from a fully instrumented measurement set, that is only available for a restricted set of run-to-failure experiments, typically gathered in an R&D setting. Although being artificial and retrospectively constructed metrics, we interpret RUL and SOH as physical measurements with the purpose to identify accurate degradation dynamics. Once the degradation model is identified, we practice the mathematical flexibility of the HMM framework to estimate several of the no longer available dynamic quantities of interest in real-time, from the limited set of measurements that are available in an operational setting. This modelling paradigm is known as virtual sensing. Predictive performance and computational efficiency are further improved by domain knowledge based pre-processing of the measurements. We apply our methodology to solenoid valves (SV), a widely used and often critical component in many industrial systems, which display a large variation in useful lifetime. Benchmark results show that the predictive capabilities of the presented methodology compares with prognostic techniques that are more computationally and memory demanding.Note to Practitioners—The motivation for this research is twofold. First there is a pending industrial need for improved diagnostic and prognostic tools. Second there is the observation that lifetime tests usually take place in an R&D setting and that expert labelling of Remaining Useful Lifetime (RUL) or State of Health (SOH) of a component or system is often based on measurement data that is not available in the industrial setting where the prognostic tools are to be deployed in the end. These two observations suggest that there is large potential in methods that can correlate the expert labelling, in particular RUL & SOH signals, with measurement data that is available in the industrial setting. Our approach has been tested in detail on the case of Solenoid Valves, which are widely used in industry and that are often safety critical. Our experiments demonstrate that the method compares with brute force approaches that overpower ours both in terms of computational as well as memory requirements. The method is furthermore generic and there is no reason to assume it would not work for other applications.

This paper treats a special case of the Imitation from Observations (IfO) problem. IfO is a generalisation of Imitation Learning from state-only demonstrations. Our treatment of IfO considers the case of feature-only demonstrations. This means that the full state is inaccessible for inference, and imitation must occur on the basis of a limited set of features. We refer to this setting as Imitation from Partial Observations (IfPO). This scenario has the advantage of allowing to address a wider variety of demonstrations, as well as solving the problem of heteromorphic student and teacher. We set out for policy learning methods that extract an executable state-feedback policy, directly from those features, which in the literature is known as Behavioural Cloning. In this theoretical work, we formalize the rational inference model of the student decision maker, devoted to imitation, as a controlled Hidden Markov Model. The IfPO problem is then reformulated as a Maximum Likelihood Estimation problem and treated using Expectation-Maximization. We name the resulting fixed point iterations A Posteriori Control Densities. We compare the presented approach to existing methods in the field and identify potential directions for further development, such as an extension to unknown transition and emission models.

In this work we formulate and treat an extension of the Imitation from Observations problem. Imitation from Observations is a generalisation of the well-known Imitation Learning problem where state-only demonstrations are considered. In our treatment we extend the scope of Imitation from Observations to feature-only demonstrations which could arguably be described as partial observations. Therewith we mean that the full state of the decision makers is unknown and imitation must take place on the basis of a limited set of features. We set out for methods that extract an executable policy directly from those features which, in the literature, would be referred to as Behavioural Cloning methods. Our treatment combines elements from probability and information theory and draws connections with entropy regularized Markov Decision Processes.

We propose a series of methods to generate inverse stochastic trajectories given parametric model uncertainties. The methods are applied for the generation of robust quad-copter trajectories. Our aim is to generate unique reference trajectories such that the corresponding uncertain control policies are less sensitive to parameter variations. Such robust trajectories exhibit interesting properties w.r.t. to tracking. The proposed methods can be applied on any system represented by nonlinear dynamics given that an inverse dynamic model is available, be it in a flat interpretation as is the case for quad-copters. Results show a decrease of 78% of parameter induced policy uncertainty when compared with nominal trajectories.