Free Energy Principle for the Noise Smoothness Estimation of Linear Systems with Colored Noise

The free energy principle (FEP) from neuroscience provides a framework called active inference for the joint estimation and control of state space systems, subjected to colored noise. However, the active inference community has been challenged with the critical task of manually tuning the noise smoothness parameter. To solve this problem, we introduce a novel online noise smoothness estimator based on the idea of free energy principle. We mathematically show that our estimator can converge to the free energy optimum during smoothness estimation. Using this formulation, we introduce a joint state and noise smoothness observer design called DEMs. Through rigorous simulations, we show that DEMs outperforms state-of-the-art state observers with least state estimation error. Finally, we provide a proof of concept for DEMs by applying it on a real life robotics problem - state estimation of a quadrotor hovering in wind, demonstrating its practical use.Comment: 6 pages, 8 figure

Active Inference and Behavior Trees for Reactive Action Planning and Execution in Robotics

We propose a hybrid combination of active inference and behavior trees (BTs) for reactive action planning and execution in dynamic environments, showing how robotic tasks can be formulated as a free-energy minimization problem. The proposed approach allows to handle partially observable initial states and improves the robustness of classical BTs against unexpected contingencies while at the same time reducing the number of nodes in a tree. In this work, the general nominal behavior is specified offline through BTs, where a new type of leaf node, the prior node, is introduced to specify the desired state to be achieved rather than an action to be executed as typically done in BTs. The decision of which action to execute to reach the desired state is performed online through active inference. This results in the combination of continual online planning and hierarchical deliberation, that is an agent is able to follow a predefined offline plan while still being able to locally adapt and take autonomous decisions at runtime. The properties of our algorithm, such as convergence and robustness, are thoroughly analyzed, and the theoretical results are validated in two different mobile manipulators performing similar tasks, both in a simulated and real retail environment

The effects of swing-leg retraction on running performance: analysis, simulation, and experiment

Using simple running models, researchers have argued that swing-leg retraction can improve running robot performance. In this paper, we investigate whether this holds for a more realistic simulation model validated against a physical running robot. We find that swing-leg retraction can improve stability and disturbance rejection. Alternatively, swing-leg retraction can simultaneously reduce touchdown forces, slipping likelihood, and impact energy losses. Surprisingly, swing-leg retraction barely affected net energetic efficiency. The retraction rates at which these effects are the greatest are strongly model-dependent, suggesting that robot designers cannot always rely on simplified models to accurately predict such complex behaviors

Unwieldy Object Delivery with Nonholonomic Mobile Base: A Stable Pushing Approach

This paper addresses the problem of pushing manipulation with nonholonomic mobile robots. Pushing is a fundamental skill that enables robots to move unwieldy objects that cannot be grasped. We propose a stable pushing method that maintains stiff contact between the robot and the object to avoid consuming repositioning actions. We prove that a line contact, rather than a single point contact, is necessary for nonholonomic robots to achieve stable pushing. We also show that the stable pushing constraint and the nonholonomic constraint of the robot can be simplified as a concise linear motion constraint. Then the pushing planning problem can be formulated as a constrained optimization problem using nonlinear model predictive control (NMPC). According to the experiments, our NMPC-based planner outperforms a reactive pushing strategy in terms of efficiency, reducing the robot's traveled distance by 23.8\% and time by 77.4\%. Furthermore, our method requires four fewer hyperparameters and decision variables than the Linear Time-Varying (LTV) MPC approach, making it easier to implement. Real-world experiments are carried out to validate the proposed method with two differential-drive robots, Husky and Boxer, under different friction conditions.Comment: The short version of the paper is accepted by RA

Pharmacological treatment of Lambert-Eaton Myasthenic Syndrome

Lambert-Eaton myasthenic syndrome (LEMS) is a very rare antibody-mediated autoimmune disease of the neuromuscular junction. Therapy can be divided in symptomatic treatment and immunosuppressive treatment. Symptomatic treatment with amifampridine is the only therapy currently authorized for use in LEMS patients. In the Netherlands the first choice drug is amifampridine base in an extended release formulation instead of the currently authorized amifampridine phosphate. This formulation has lower costs and is possibly safer due to lower peak concentrations. Other therapy used in LEMS patients is prescribed off-label and is based on experience in patients with myasthenia gravis. In many cases pyridostigmine is added as symptomatic treatment. In almost half of patients immunosuppressive therapy is started, mostly corticosteroids with or without azathioprine. Intravenous immunoglobulins and plasma exchange are used as emergency treatment. Currently no randomized clinical trials with new therapies are ongoing or announced in patients with LEMS, although multiple new therapies for myasthenia gravis are being investigated. These future therapies can be differentiated in symptomatic and immunomodulating drugs. The immunomodulating drugs can be further differentiated in early stage drugs which target the B-cell, later stage drugs which target the circulating antibodies and targeted therapy which have a disease-specific target. Some early and later stage immunomodulating drugs show promising results in myasthenia gravis although high cost and uncertain long term safety may be limiting for incorporating these drugs in LEMS treatment guidelines. Clinical trials in LEMS patients are lacking due to the rarity of the disease and we suggest the following requirements for future trials of potential new treatments: Sufficient power by performing multicenter or n-of-1 trials when appropriate, a cross-over design to reduce the number of patients and using a LEMS-specific quantitative primary outcome measure like the 3TUG score