Abstract The estimation of object orientation from RGB images is a core component in many modern computer vision pipelines. Traditional techniques mostly predict a single orientation per image, learning a one-to-one mapping between images and rotations. However, when objects exhibit rotational symmetries, they can appear identical from multiple viewpoints. This induces ambiguity in the estimation problem, making images map to rotations in a one-to-many fashion. In this paper, we explore several ways of addressing this problem. In doing so, we specifically consider algorithms that can map an image to a range of multiple rotation estimates, accounting for symmetry-induced ambiguity. Our contributions are threefold. Firstly, we create a data set with annotated symmetry information that covers symmetries induced through self-occlusion. Secondly, we compare and evaluate various learning strategies for multiple-hypothesis prediction models applied to orientation estimation. Finally, we propose to model orientation estimation as a binary classification problem. To this end, based on existing work from the field of shape reconstruction, we design a neural network that can be sampled to reconstruct the full range of ambiguous rotations for a given image. Quantitative evaluation on our annotated data set demonstrates its performance and motivates our design choices.

Active suction cups are widely adopted in industrial and logistics automation. Despite that, validated dynamic models describing their 6D force/torque interaction with objects are rare. This work aims at filling this gap by showing that it is possible to employ a compact model for suction cups, providing good accuracy also for large deformations. Its potential use is for advanced manipulation, planning, and control. We model the interconnected object-suction cup system as a lumped 6D mass-spring-damper systems, employing a potential energy function on$\text {SE}(3)$, parametrized by a$6\times 6$stiffness matrix. By exploiting geometric symmetries of the suction cup, we reduce the parameter identification problem, from$6(6+1) / 2 = 21$to only$\boldsymbol {5}$independent parameters, greatly simplifying the parameter identification procedure, that is otherwise ill-conditioned. Experimental validation is provided and data is shared openly to further stimulate research. As an indication of the achievable pose prediction in steady state, for an object of about$\boldsymbol {1.75}$kg, we obtain a pose error in the order of$\boldsymbol {5}$mm and$\boldsymbol {3}$deg, with a gripper inclination of$\boldsymbol {60}$deg.

With the aim of further enabling the exploitation of intentional impacts in robotic manipulation, a control framework is presented that directly tackles the challenges posed by tracking control of robotic manipulators that are tasked to perform nominally simultaneous impacts. This framework is an extension of the reference spreading (RS) control framework, in which overlapping ante- and post-impact references that are consistent with impact dynamics are defined. In this work, such a reference is constructed starting from a teleoperation-based approach. By using the corresponding ante- and post-impact control modes in the scope of a quadratic programming control approach, peaking of the velocity error and control inputs due to impacts is avoided while maintaining high tracking performance. With the inclusion of a novel interim mode, we aim to also avoid input peaks and steps when uncertainty in the environment causes a series of unplanned single impacts to occur rather than the planned simultaneous impact. This work in particular presents for the first time an experimental evaluation of RS control on a robotic setup, showcasing its robustness against uncertainty in the environment compared to three baseline control approaches.

This paper focuses on the problem of smoothing a rotation trajectory corrupted by noise, while simultaneously estimating its corresponding angular velocity and angular acceleration. To this end, we develop a geometric version of the Savitzky-Golay filter on SO(3) that avoids following the conventional practice of first converting the rotation trajectory into Euler-like angles, performing the filtering in this new set of local coordinates, and finally converting the result back on SO (3). In particular, the estimation of the angular acceleration requires the computation of the right-trivialized second covariant derivative of the exponential map on SO (3) with respect to the (+) Cartan-Schouten connection. We provide an explicit expression for this derivative, creating a link to seemingly unrelated existing results concerning the first derivative of the exponential map on SE (3). A numerical example is provided in which we demonstrate the effectiveness and straightforward applicability of the proposed approach. An open implementation of the new geometric Savitzky-Golay filter is also provided.

Suction grippers are the most common pick-and-place end effectors used in industry. However, there is little literature on creating and validating models to predict their force interaction with objects in dynamic conditions. In this paper, we study the interaction dynamics of an active vacuum suction gripper during the vertical release of an object. Object and suction cup motions are recorded using a motion capture system. As the object's mass is known and can be changed for each experiment, a study of the object's motion can lead to an estimate of the interaction force generated by the suction gripper. We show that, by learning this interaction force, it is possible to accurately predict the object's vertical motion as a function of time. This result is the first step toward 3D motion prediction when releasing an object from a suction gripper.

Accurate post-impact velocity predictions are essential in developing impact-aware manipulation strategies for robots, where contacts are intentionally established at non-zero speed mimicking human manipulation abilities in dynamic grasping and pushing of objects. Starting from the recorded dynamic response of a 7DOF torque-controlled robot that intentionally impacts a rigid surface, we investigate the possibility and accuracy of predicting the post-impact robot velocity from the pre-impact velocity and impact configuration. The velocity prediction is obtained by means of an impact map, derived using the framework of nonsmooth mechanics, that makes use of the known rigid-body robot model and the assumption of a frictionless inelastic impact.The main contribution is proposing a methodology that allows for a meaningful quantitative comparison between the recorded post-impact data, that exhibits a damped oscillatory response after the impact, and the post-impact velocity prediction derived via the readily available rigid-body robot model, that presents no oscillations and that is the one typically obtained via mainstream robot simulator software. The results of this new approach are promising in terms of prediction accuracy and thus relevant for the growing field of impact-aware robot control. The recorded impact data (18 experiments) is made publicly available, together with the numerical routines employed to generate the quantitative comparison, to further stimulate interest/research in this field.

We propose a two-layer control architecture for bilateral teleoperation with communication delays. The controller is structured with an (inner) performance layer and an (outer) passivity layer. In the performance layer, any traditional controller for bilateral teleoperation can be implemented. The passivity layer guarantees that, from the operator and environment perspective, the overall teleoperator is passive: The amount of energy that can be extracted from the teleoperator is bounded from below and the rate of increase of the stored energy in the teleoperator is bounded by (twice) the environment and operator supplied power. Passivity is ensured by modulating the performance layer outputs and by injecting a variable amount of damping via an energy-based logic that follows the innovative principle of energy duplication and takes into account the detrimental effects of time delays. In contrast to the traditional teleoperation approach, in which the master and slave controllers implement an as-stiff-as-possible coupling between the master and slave devices, our scheme is specifically designed for direct force-reflecting bilateral teleoperation: The slave controller mimics the operator action, whereas the master controller reflects the slave-environment interaction. We illustrate the performance of the two-layer approach in a challenging experiment with a round-trip communication delay of 300 ms while making and breaking contact with a stiff aluminum environment. Finally, we also compare our controller with the state of the art.

This work explores the stabilization of desired dynamic motion tasks involving hard impacts at non-negligible speed for humanoid robots. To this end, a so-called reference spreading hybrid control law is designed showing promising results in simulation. The simulations are performed employing a dynamical model of an existing humanoid robot and impacts are assumed to be inelastic. The desired motion task consists of having the robot balancing on one foot while repeatedly making and breaking contact with a wall by means of one hand. The simulation results illustrate that the considered controller is suited to control humanoid robot motions with impacts.