Lifting objects, whose mass may produce high wrist torques that exceed the hardware strength limits, could lead to unstable grasps or serious robot damage. This work introduces a new Center-of-Mass...
Other types of sensing were also used to complete manipulation tasks. For instance, tactile sensing @cite_46 @cite_57 @cite_13 was used for small object localization and manipulation (see Ref. @cite_29 for a review). Force torque sensing was mainly used in the literature for the detection of contact, @cite_69 slippage, @cite_67 or shape @cite_58 prediction. An alternative approach to more stable grasping is through the object model learning. For instance, tracking contacts while estimating the object's dimensions, mass, and friction @cite_37 or updating the object's attribute using tactile sensing @cite_49 was studied in a probabilistic framework. Towards active manipulation, @cite_26 object six Degrees-of-Freedom (DoF) localization takes place using various methods, such as the Scaling Series via touching @cite_11 or using Bayesian approaches for tactile sensing. @cite_31 In a much different direction, learning methods were also used for whole-body manipulation, for instance to learn friction models of the objects. @cite_65
@cite_69 @cite_67 @cite_37 @cite_26 @cite_31 @cite_29 @cite_65 @cite_57 @cite_49 @cite_46 @cite_58 @cite_13 @cite_11
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It is frequently accepted in the manipulation literature that tactile sensing is needed to improve the precision of robot manipulation. However, there is no consensus on how this may be achieved. This paper applies particle filtering to the problem of localizing the pose and shape of an object that the robot touches. We are motivated by the situation where the robot has enclosed its fingers around an object but has not yet grasped it. This might be the case just prior to grasping or when the robot is holding on to something fixtured elsewhere in the environment. In order to solve this problem, we propose a new model for position measurements of points on the robot manipulator that tactile sensing indicates are touching the object. We also propose a model for points on the manipulator that tactile measurements indicate are not touching the object. Finally, we characterize the approach in simulation and use it to localize an object that Robonaut 2 holds in its hand. Can a robot grasp an unknown object without seeing it? In this paper, we present a tactile-sensing based approach to this challenging problem of grasping novel objects without prior knowledge of their location or physical properties. Our key idea is to combine touch based object localization with tactile based re-grasping. To train our learning models, we created a large-scale grasping dataset, including more than 30 RGB frames and over 2.8 million tactile samples from 7800 grasp interactions of 52 objects. To learn a representation of tactile signals, we propose an unsupervised auto-encoding scheme, which shows a significant improvement of 4-9 over prior methods on a variety of tactile perception tasks. Our system consists of two steps. First, our touch localization model sequentially 'touch-scans' the workspace and uses a particle filter to aggregate beliefs from multiple hits of the target. It outputs an estimate of the object's location, from which an initial grasp is established. Next, our re-grasping model learns to progressively improve grasps with tactile feedback based on the learned features. This network learns to estimate grasp stability and predict adjustment for the next grasp. Re-grasping thus is performed iteratively until our model identifies a stable grasp. Finally, we demonstrate extensive experimental results on grasping a large set of novel objects using tactile sensing alone. Furthermore, when applied on top of a vision-based policy, our re-grasping model significantly boosts the overall accuracy by 10.6 . We believe this is the first attempt at learning to grasp with only tactile sensing and without any prior object knowledge. Robust manipulation and insertion of small parts can be challenging because of the small tolerances typically involved. The key to robust control of these kinds of manipulation interactions is accurate tracking and control of the parts involved. Typically, this is accomplished using visual servoing or force-based control. However, these approaches have drawbacks. Instead, we propose a new approach that uses tactile sensing to accurately localize the pose of a part grasped in the robot hand. Using a feature-based matching technique in conjunction with a newly developed tactile sensing technology known as GelSight that has much higher resolution than competing methods, we synthesize high-resolution height maps of object surfaces. As a result of these high-resolution tactile maps, we are able to localize small parts held in a robot hand very accurately. We quantify localization accuracy in benchtop experiments and experimentally demonstrate the practicality of the approach in the context of a small parts insertion problem. This paper presents a novel framework for integration of vision and tactile sensing by localizing tactile readings in a visual object map. Intuitively, there are some correspondences, e.g., prominent features, between visual and tactile object identification. To apply it in robotics, we propose to localize tactile readings in visual images by sharing same sets of feature descriptors through two sensing modalities. It is then treated as a probabilistic estimation problem solved in a framework of recursive Bayesian filtering. Feature-based measurement model and Gaussian based motion model are thus built. In our tests, a tactile array sensor is utilized to generate tactile images during interaction with objects and the results have proven the feasibility of our proposed framework.