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.

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.

We present a friction model for the curved contact area between a deformable object and soft parallel gripper jaws for grasping posture estimation. We show that the assumption of a planar contact area leads to an overestimation of the frictional force and torque, which might cause the object to slip. We simulate the contact with the Finite Element Method, then compute the friction wrenches, which are fitted with two limit surface models: an ellipsoid and a convex 4th-order polynomial. Despite a slightly higher fitting error, the ellipsoid limit surface is chosen to compute the grasp quality because of its simplicity. We compare the limit surfaces of our friction model with the planar contact model and show the improved accuracy obtainable with our model. We then apply the presented model for grasping posture estimation by simulating the contact for all grasp candidates. We show a grasp quality map (quality of all grasp candidates) and the best possible grasp location for several deformable objects.

This article proposes an application-layer multiplexing scheme for teleoperation systems with multimodal feedback (video, audio, and haptics). The available transmission resources are carefully allocated to avoid delay-jitter for the haptic signal potentially caused by the size and arrival time of the video and audio data. The multiplexing scheme gives high priority to the haptic signal and applies a preemptive-resume scheduling strategy to stream the audio and video data. The proposed approach estimates the available transmission rate in real time and adapts the video bitrate, data throughput, and force buffer size accordingly. Furthermore, the proposed scheme detects sudden transmission rate drops and applies congestion control to avoid abrupt delay increases and converge promptly to the altered transmission rate. The performance of the proposed scheme is measured objectively in terms of end-to-end signal latencies, packet rates, and peak signal-to-noise ratio (PSNR) for visual quality. Moreover, peak-delay and convergence time measurements are carried out to investigate the performance of the congestion control mode of the system.

The visual 3D reconstruction of transparent objects in unstructured scenes is challenging due to the complex image formation principles underlying their visual appearance. Most state-of-the-art reconstruction methods ignore this problem and assume Lambertian reflection. Yet, transparent objects are relevant scene information for applications in intelligent robotics (such as grasping) or virtual reality. In this work, we present an approach to detect non-planar transparent objects, like bottles or glasses, by specifically searching for geometry inconsistencies caused by refraction or reflection. Depth information is acquired using a Kinect sensor, which is moved within the scene in order to acquire multiple views. The individual measurements are combined into a 3D volume, yielding the objects' location and a rough shape estimate. Results are presented using various household objects made of glass or plastic.

We propose a method that rates the suitability of given templates for template-based tracking in real-time. This is important for applications with online template selection, such as SLAM, where it is essential to track a low number of preferably reliable templates. Our approach is based on simple image features specifically designed to identify texture properties which are problematic for tracking. During a training step, a support vector régresser is learned. It uses a tracking quality measure which considers both convergence rate and speed obtained by simulation of many tracking attempts. Finally, a minimum set of image features is identified to speedup the online selection process. In experiments on real-world video sequences our method improved the detection rate of an existing tracking-by-detection system by 8% on average.

Hardware/software partitioning of algorithms is gaining more and more importance in order to benefit from the advantages of both worlds. Pure software implementations are easy to change but the processing time is rather high. By contrast pure hardware implementations usually result in faster processing due to inherent parallelism but they do not offer the necessary flexibility for quick changes and adaptions. In this paper the hardware/software co-design of a self-developed algorithm to detect cars by their taillights as well as its implementation on an embedded system (FPGA) is presented. Instead of utilizing expensive sensors such as RADAR which also can be used to detect obstacles in dark environments, the detection method presented here is based solely on grayscale images taken by a low-cost on-board camera which was mounted on a moving vehicle. Only computationally intense parts - namely pixel or sliding window operations - are implemented in hardware to achieve the necessary real-time requirements. The remainder of the algorithm - the so called higher level application code - is running on standard embedded CPU cores. With this architecture it is possible to process the incoming video-stream (25 frames/s) and detect cars in real-time on an embedded system.