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We propose a new benchmarking protocol to evaluate algorithms for bimanual robotic manipulation semi-deformable objects. The benchmark is inspired from two real-world applications: (a) watchmaking craftsmanship, and (b) belt assembly in automobile engines. We provide two setups that try to highlight the following challenges: (a) manipulating objects via a tool, (b) placing irregularly shaped objects in the correct groove, (c) handling semi-deformable objects, and (d) bimanual coordination. We provide CAD drawings of the task pieces that can be easily 3D printed to ensure ease of reproduction, and detailed description of tasks and protocol for successful reproduction, as well as meaningful metrics for comparison. We propose four categories of submission in an attempt to make the benchmark accessible to a wide range of related fields spanning from adaptive control, motion planning to learning the tasks through trial-and-error learning.

The tasks of exploring unknown workspaces and recognizing objects based on their physical properties are challenging for autonomous robots. In this paper, we present strategies solely based on tactile information to enable robots to accomplish such tasks. (1) An active exploration approach for the robot to explore unknown workspaces; (2) an active touch objects learning method that enables the robot to learn efficiently about unknown objects via their physical properties (stiffness, surface texture, and center of mass); and (3) an active object recognition strategy, based on the knowledge the robot has acquired. Furthermore, we propose a tactile-based approach for estimating the center of mass of rigid objects. Following the active touch for workspace exploration, the robotic system with the sense of touch in fingertips reduces the uncertainty of the workspace up to 65 and 70% compared respectively to uniform and random strategies, for a fixed number of samples. By means of the active touch learning method, the robot achieved 20 and 15% higher learning accuracy for the same number of training samples compared to uniform strategy and random strategy, respectively. Taking advantage of the prior knowledge obtained during the active touch learning, the robot took up to 15% fewer decision steps compared to the random method to achieve the same discrimination accuracy in active object discrimination task.

In this letter, we propose a complete probabilistic tactile-based framework to enable robots to autonomously explore unknown workspaces and recognize objects based on their physical properties. Our framework consists of three components: 1) an active pretouch strategy to efficiently explore unknown workspaces; 2) an active touch learning method to learn about unknown objects based on their physical properties (surface texture, stiffness, and thermal conductivity) with the least number of training samples; and 3) an active touch algorithm for object discrimination, which selects the most informative exploratory action to apply to the object, so that the robot can efficiently distinguish between objects with a few number of actions. Our proposed framework was experimentally evaluated using a robotic arm equipped with multimodal artificial skin. The robot with the active pretouch method reduced the uncertainty of the workspace up to 30% and 70% compared to uniform and random strategies, respectively. By means of the active touch learning algorithm, the robot used 50% fewer samples to achieve the same learning accuracy than the baseline methods. By taking advantage of the prior knowledge obtained during the learning process, the robot actively discriminated objects with an improvement of 10% recognition accuracy compare to the random action selection approach.

International audience; — In robotic tasks, object recognition and discrimination can be realized according to their physical properties, such as color, shape, stiffness, and surface textures. However, these external properties may fail if they are similar or even identical. In this case, internal properties of the objects can be considered, for example, the center of mass. Center of mass is an important inherent physical property of objects; however, due to the difficulties in its determination, it has never been applied in object discrimination tasks. In this work, we present a tactile-based approach to explore the center of mass of rigid objects and apply it in robotic object discrimination tasks. This work comprises three aspects: (a) continuous estimation of the target object's geometric information, (b) exploration of the center of mass, and (c) object discrimination based on the center of mass features. Experimental results show that by following our proposed approach, the center of mass of experimental objects can be accurately estimated, and objects of identical external properties but different mass distributions can be successfully discriminated. Our approach is also robust against the textural properties and stiffness of experimental objects.

International audience; Tactile sensing feedback provides feasible solutions to robotic dexterous manipulation tasks. In this paper, we present a novel tactile-based framework for detecting/correcting slips and regulating grasping forces while manipulating de-formable objects with the dynamic center of mass. This framework consists of a tangential force based slip detection method and a deformation prevention approach relying on weight estimation. Moreover, we propose a new strategy for manipulating deformable heavy objects. Objects with different stiffnesses, surface textures, and centers of mass are tested in experiments. Results show that proposed approaches are capable of handling objects with uncertainties in their characteristics, and also robust to external disturbances.