Incoherent input is a common source of undesirable output of Latent Diffusion Models. We propose an approach for detecting and mitigating such inputs. For prompt incoherences, we analyze key categories for conflicting descriptions. We mitigate conflicts using a graph-based approach, minimizing the required modifications. Image incoherences are detected by assessing if a global or local style is present. Regions with no distinct style are discarded and replaced with a text prompt describing the image content. We evaluate the framework on over 11,000 images generated with Stable Diffusion XL using the PartiPrompts and StyleBench datasets. The framework detects 91% of all incoherences. For text input, the aesthetic score increased by up to 1.5% and the CLIP score by 4.7%. For image input, the aesthetic score improved by up to 10.1% and the CLIP score by 9.1%. In two validation surveys with 106 participants, the output of the proposed framework was preferred for 86% of the images.

Robot grasping with deformable gripper jaws results in nonplanar surface contacts if the jaws deform to the nonplanar local geometry of an object. The frictional force and torque that can be transmitted through a nonplanar surface contact are both 3-D, resulting in a 6-D frictional wrench (6DFW). Applying traditional planar contact models to such contacts leads to overconservative results as the models do not consider the nonplanar surface geometry and only compute a 3-D subset of the 6DFW. To address this issue, we derive the 6DFW for nonplanar surfaces by combining concepts of differential geometry and Coulomb friction. We also propose two 6-D limit surface (6DLS) models, generalized from well-known 3-D LS (3DLS) models, which describe the friction-motion constraints for a contact. We evaluate the 6DLS models by fitting them to the 6DFW samples obtained from six parametric surfaces and 2932 meshed contacts from finite element method simulations of 24 rigid objects. We further present an algorithm to predict multicontact grasp success by building a grasp wrench space with the 6DLS model of each contact. To evaluate the algorithm, we collected 1035 physical grasps of ten 3-D-printed objects with a KUKA robot and a deformable parallel-jaw gripper. In our experiments, the algorithm achieves 66.8% precision, a metric inversely related to false positive predictions, and 76.9% recall, a metric inversely related to false negative predictions. The 6DLS models increase recall by up to 26.1% over 3DLS models with similar precision.1

The acceptance of teleoperation applications like tele-driving, tele-surgery, tele-maintenance, etc., is challenged by the quality-reducing effect of end-to-end latency. Particularly, when users wear Head-Mounted Displays to enhance the immersive experience, the lag between head motion and display response leads to unbearable motion sickness, indisposition, and, in the worst case, abortion of the teleoperation session. In this paper, we propose a delay compensation approach with head motion prediction that can be applied to pan-tilt-unit-based stereoscopic telepresence systems. We provide the user with the impression of a 3D 360° video that represents the remote scene without noticing the present delay, even when rotating the head. To this end, we propose a novel prediction paradigm for head motion estimation to substantially mitigate the negative impact of the latency on the quality of experience. We re-implemented state-of-the-art head movement predictors and compare them to our proposed approach by means of qualitative measures. In our experiments, we used two real and independent head motion datasets for validation and tested communication delays between 100-1000ms. Our results show that mean compensation rates of more than 99% are able with our approach.

In the field of autonomous robotics, Simultaneous Localization and Mapping (SLAM) is still a challenging problem. With cheap visual sensors attracting more and more attention, various solutions to the SLAM problem using visual cues have been proposed. However, current visual SLAM systems are still computationally demanding, especially on embedded devices. In addition, collaborative SLAM approaches emerge using visual information acquired from multiple robots simultaneously to build a joint map. In order to address both challenges, we present an approach for remote visual SLAM where local binary features are extracted at the robot, compressed and sent over a network to a centralized powerful processing node running the visual SLAM algorithm. To this end, we propose a new feature coding scheme including a feature selection stage which ensures that only relevant information is transmitted. We demonstrate the effectiveness of our approach on well-known datasets. With the proposed approach, it is possible to build an accurate map while limiting the data rate to 75 kbits/frame.

Assessing grasp stability is essential to prevent the failure of robotic manipulation tasks due to sensory data and object uncertainties. Learning-based approaches are widely deployed to infer the success of a grasp. Typically, the underlying model used to estimate the grasp stability is trained for a specific task, such as lifting, hand-over, or pouring. Since every task has individual stability demands, it is important to adapt the trained model to new manipulation actions. If the same trained model is directly applied to a new task, unnecessary grasp adaptations might be triggered, or in the worst case, the manipulation might fail. To address this issue, we divide the manipulation task used for training into seven sub-tasks, defined as modular tasks. We deploy a learning-based approach and assess the stability for each modular task separately. We further propose analytical features to reduce the dimensionality and the redundancy of the tactile sensor readings. A main task can thereby be represented as a sequence of relevant modular tasks. The stability prediction of the main task is computed based on the inferred success labels of the modular tasks. Our experimental evaluation shows that the proposed feature set lowers the prediction error up to 5.69% compared to other sets used in state-of-the-art methods. Robotic experiments demonstrate that our modular task-oriented stability assessment avoids unnecessary grasp force adaptations and regrasps for various manipulation tasks.

Communication delay is a major challenge for the acceptance of telepresence applications. It is particularly critical when the user experiences the remote environment via a Head-Mounted-Display. The lag between head motion and display response results in motion sickness, indisposition, and, at worst, abortion of the telepresence session. In this paper, we propose a delay compensation approach for 3D 360° telepresence systems realized with a mechanically actuated stereoscopic vision system. We further introduce a novel metric to evaluate the achievable level of delay compensation. We investigate state-of-the-art head motion predictors and propose a novel probabilistic prediction paradigm, which can half the mean prediction error and improve the level of delay compensation by up to 26%. The general validity of our approach is shown by means of two independent real head motion datasets. The experimental results verify that average compensation rates of more than 99% can be achieved for communication delays between 100-500ms.

Swarm robotics is receiving increasing interest, because the collaborative completion of tasks, such as the exploration of unknown environments, leads to improved performance and reduced effort. The ability to exchange map information is an essential requirement for collaborative exploration. When moving to large-scale environments, where the communication data rate between the swarm participants is typically limited, efficient compression algorithms and an approach for discarding less informative parts of the map are key for a successful long-term operation. In this paper, we present a novel compression approach for environment maps obtained from a visual SLAM system. We apply feature coding to the visual information to compress the map efficiently. We make use of a minimum spanning tree to connect all features that serve as observations of a single map point. Thereby, we can exploit inter-feature dependencies and obtain an optimal coding order. Additionally, we add a map sparsification step to keep only useful map points by solving a linear integer programming problem, which preserves the map points that exhibit both good compression properties and high observability. We evaluate the proposed method on a standard dataset and show that our approach outperforms state-of-the-art techniques.