The strength of the human hand lies in its ability to manipulate objects precisely and robustly. In contrast, simple robotic grippers have low dexterity and fail to handle small objects effectively. This is why many automation tasks remain unsolved by robots. This paper presents an optimization-based framework for in-hand manipulation with a robotic hand equipped with compact Magnetic Tactile Sensors (MTSs). We formulate a trajectory optimization problem using Nonlinear Programming (NLP) for finger movements while ensuring contact points to change along the geometry of the fingers. Using the optimized trajectory from the solver, we implement and test an open-loop controller for rolling motion. To further enhance robustness and accuracy, we introduce a force controller for the fingers and a state estimator for the object utilizing MTSs. The proposed framework is validated through comparative experiments, showing that incorporating the force control with compliance consideration improves the accuracy and robustness of the rolling motion. Rolling an object with the force controller is 30% more likely to succeed than running an open-loop controller. The demonstration video is available at https://youtu.be/6J_muL_AyE8.

This paper presents a control barrier function (CBF) for systems described by differential-algebraic equations and applies the method to guarantee the safety of a two-link flexible-link manipulator. The two main contributions of the paper are: a) an extension of CBFs to systems governed by differential-algebraic equations; b) a framework for simulation of flexible-link robots in a floating frame of reference formulation (FFRF) finite element method (FEM). Numerical simulations demonstrate the minimally invasive safety control of a flexible two-link manipulator with position constraints through CBF quadratic programming without converting the differential-algebraic equations to a control-affine system.

This paper presents a method for contact-based pose estimation of workpieces using a collaborative robot. The proposed pose estimation exploits positions and surface normal vectors along an arbitrary path on an object with known geometry, where surface normal vectors are estimated based on contact forces measured by the robot. When data is only available along a single path, it is difficult to find initial correspondences between source data (recorded points and normal vectors) and target data (CAD of an object); hence, a novel weighted incremental spatial search approach for generating correspondences based on point pair features is proposed. Subsequently, robust pose estimation is employed to reduce the effect of erroneous correspondences. The proposed pose estimation is verified in simulation on three paths on two objects and with different levels of noise on the source data to quantify the robustness of the algorithm. Finally, the method is experimentally validated to provide an average pose rotation and translation accuracy of$\mathbf{0.55}^{\circ}$and 0.51 mm, respectively, when using the robust estimation cost function Geman-McClure.

This paper presents a framework for transferring surface finishing skills to new surface geometries while preserving the surface finish quality. The main idea is to estimate the contact area between the workpiece and the tool by using 3D point cloud approach and replicate a given material removal rate and the accumulated material removal, as these quantities are the main parameters for quality. The grinding motion trajectory is generated by solving a constrained optimization problem that minimizes the maximal point-wise deviation between actual and desired material removal and simultaneously minimizes the average deviation between actual and desired material removal rate. The proposed approach is verified in simulation to show the difference between direct replication of force/motion and the proposed replication of material removal. Finally, experimental results confirm that the quality of a surface finishing task can be transferred to new surface geometries with the proposed method.

In this paper we present an initial approach towards reversible robot movement primitives. Our approach is a modification of Dynamic Movement Primitives (DMPs), a widely used framework for robot learning from demonstration. DMPs are based on dynamical systems to guarantee properties such as convergence to a goal state, robustness to perturbation, and the ability to generalize to other goal states. Yet a main limitation of their original formulation is that they do not allow for movements to be reversed. Thus, to execute the same task forwards and backwards would mean to learn two separate primitives. We propose to replace the transformation system in DMPs with the Logistic Differential Equation (LDE), a known time-reversible non-linear system. Similarly to the original DMP formulation, our system's temporal evolution is controlled by a phase system, which in our case is derived from the LDE to guarantee reversibility. We evaluate our approach experimentally with demonstration data from a real robot assembly task, and show comparable properties to those of the original DMP system.

This paper presents a method for numerically computing the highest path velocity that a collaborative robot can attain, while complying with safety requirements. The safety requirements are obtained from ISO/TS 15066 that describes a collaborative method called power and force limiting, which specifies safe collisions between humans and robots. In particular, we assume that a path is given and compute the point-wise maximal path velocity that ensures a safe impact, i.e., the paper provides no considerations on the post impact safety.

We consider the safety problem of piecewise-deterministic Markov processes (PDMP). These are systems that have deterministic dynamics and stochastic jumps, where both the time and the destination of the jumps are stochastic. Specifically, we solve a p-safety problem, where we identify the set of initial states from which the probability to reach designated unsafe states is at most 1 - p. Based on the knowledge of the full generator of the PDMP, we are able to develop a system of partial differential equations describing the connection between unsafe and initial states. We then show that by using the moment method, we can translate the infinite-dimensional optimisation problem searching for the largest set of p-safe states to a finite dimensional polynomial optimisation problem. We have implemented this technique on top of GloptiPoly and show how to apply it to a numerical example.

This paper presents a solution to the control to facet problem for arbitrary polynomial vector fields defined on simplices. The novelty of the work is to use Bernstein coefficients of polynomials for determining certificates of positivity. Specifically, the constraints that are set up for the controller design are solved by searching for polynomials in Bernstein form. This allows the controller design problem to be formulated as a linear programming problem. Examples are provided that demonstrate the efficiency of the method for designing controls for polynomial systems.

This paper proposes a compositional method for verifying the safety of a dynamical system, given as an interconnection of subsystems. The safety verification is conducted by the use of the barrier certificate method; hence, the contribution of this paper is to show how to obtain compositional conditions for safety verification.