Grasping Objects of Unknown Geometry with Tactile Feedback

Classical robotic approaches to tactile object identification often involve rigid mechanical grippers, dense sensor arrays and exploratory procedures (EPs). Though EPs are a natural method for humans to acquire object information, evidence also exists for meaningful tactile property inference from brief, non-exploratory motions (a ‘haptic glance’). In this work we implement tactile object identification and feature extraction techniques on data acquired during a single, unplanned grasp with a simple, underactuated robot hand equipped with inexpensive barometric pressure sensors. Our methodology utilizes two cooperating schemes based on an advanced machine learning technique (random forests) and parametric methods that estimate object properties. The available data is limited to actuator positions (one per two link finger) and force sensors values (8 per finger). The schemes are able to work both independently and collaboratively, depending on the task scenario. When collaborating, the results of each method contribute to the other, improving the overall result in a synergistic fashion. Unlike prior work, the proposed approach does not require object exploration, re-grasping, grasp release or force modulation and works for arbitrary object start positions and orientations. Due to these factors the technique may be integrated into practical robotic grasping scenarios without adding time or manipulation overheads.

Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006.

We consider the problem of autonomously estimating position and orientation of an object from tactile data. When initial uncertainty is high, estimation of all six parameters precisely is computationally expensive. We propose an efficient Bayesian approach that is able to estimate all six parameters in both unimodal and multimodal scenarios. The approach is termed Scaling Series sampling as it estimates the solution region by samples. It performs the search using a series of successive refinements, gradually scaling the precision from low to high. Our approach can be applied to a wide range of manipulation tasks. We demonstrate its portability on two applications: (1) manipulating a box and (2) grasping a door handle.

2021 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS), 2021

In this paper, a cyber-physical system composed by a tactile sensor, a robotic gripper and suitable ROS software nodes is proposed. The tactile sensors are shown to be compatible with three different commercial grippers, and the developed ROS nodes for the data acquisition and elaboration enable the implementation of complex tasks such as the grasping and the shape reconstruction of deformable linear objects like cables. The effectiveness of the systems is tested with cable of different diameters and with wiring harnesses composed by several cables grouped together, focusing on the reconstruction of linear and quadratic curves representing the cable shape. Experimental trials are also executed to show the possibility of exploiting the shape reconstruction provided by the proposed system to correct the gripper grasping pose.

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2011

Our aim is to predict the stability of a grasp from the perceptions available to a robot before attempting to lift up and transport an object. The percepts we consider consist of the tactile imprints and the object-gripper configuration read before and until the robot's manipulator is fully closed around an object. Our robot is equipped with multiple tactile sensing arrays and it is able to track the pose of an object during the application of a grasp. We present a kernel-logistic-regression model of pose-and touch-conditional grasp success probability which we train on grasp data collected by letting the robot experience the effect on tactile and visual signals of grasps suggested by a teacher, and letting the robot verify which grasps can be used to rigidly control the object. We consider models defined on several subspaces of our input data -e.g., using tactile perceptions or pose information only. Our experiment demonstrates that joint tactile and pose-based perceptions carry valuable grasp-related information, as models trained on both hand poses and tactile parameters perform better than the models trained exclusively on one perceptual input.

Sensors (Basel, Switzerland), 2018

This work presents a novel and simple approach in the area of manipulation of unknown objects considering both geometric and mechanical constraints of the robotic hand. Starting with an initial blind grasp, our method improves the grasp quality through manipulation considering the three common goals of the manipulation process: improving the hand configuration, the grasp quality and the object positioning, and, at the same time, prevents the object from falling. Tactile feedback is used to obtain local information of the contacts between the fingertips and the object, and no additional exteroceptive feedback sources are considered in the approach. The main novelty of this work lies in the fact that the grasp optimization is performed on-line as a reactive procedure using the tactile and kinematic information obtained during the manipulation. Experimental results are shown to illustrate the efficiency of the approach.

2012

We propose a simple but efficient control strategy to manipulate objects of unknown shape, weight, and friction properties-prerequisites which are necessary for classical offline grasping and manipulation methods. Instead, the proposed control strategy employs estimated contact point locations, which can be obtained from modern tactile sensors with good spatial resolution. The feasibility of the strategy is proven in simulation experiments employing a physics engine providing exact contact information. However, to motivate the applicability in real world scenarios, where only coarse and noisy contact information will be available, we also evaluated the performance of the approach when adding artificial noise.

IEEE International Conference on Robotics and Automation (ICRA), 2014

The majority of the works on grasping consider both object as well as robot hand parameters to be accurately known and do not take into account the constraints imposed by the robot hand. In this paper, a complete methodology is proposed that handles the grasping problem under a wide range of uncertainties. Initially, we search for an acceptable posture that provides robustness against positioning inaccuracies and maximizes the ability of the robot hand to exert forces on the object. Subsequently, in order to secure the grasp stability, we also deal with the determination of sufficient contact forces. Finally, an appropriate tactile sensor setup, mounted on the robot hand, allow us to reduce the magnitude of uncertainty regarding the grasping parameters. The efficiency of our approach is validated through extensive experimental paradigms using a 15 DoF DLR/HIT II robotic hand attached at the end effector of a 7 DoF Mitsubishi PA10 robotic manipulator.

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013

Object shape information is an important parameter in robot grasping tasks. However, it may be difficult to obtain accurate models of novel objects due to incomplete and noisy sensory measurements. In addition, object shape may change due to frequent interaction with the object (cereal boxes, etc). In this paper, we present a probabilistic approach for learning object models based on visual and tactile perception through physical interaction with an object. Our robot explores unknown objects by touching them strategically at parts that are uncertain in terms of shape. The robot starts by using only visual features to form an initial hypothesis about the object shape, then gradually adds tactile measurements to refine the object model. Our experiments involve ten objects of varying shapes and sizes in a real setup. The results show that our method is capable of choosing a small number of touches to construct object models similar to real object shapes and to determine similarities among acquired models.

Better sensing is crucial to improve robotic grasping and manipulation. Most robots currently have very limited perception in their manipulators, typically only fingertip position and velocity. Additional sensors make richer interactions with the objects possible. In this paper, we present a versatile, robust and low cost sensor for robot fingertips, that can improve robotic grasping and manipulation in several ways: 3D reconstruction of the shape of objects, material surface classification, and object slip detection. We extended TUM-Rosie, our robot for mobile manipulation, with fingertip sensors on its humanoid robotic hand, and show the advantages of the fingertip sensor integrated in our robot system.

2013 IEEE International Conference on Robotics and Automation, 2013

This paper presents a unified framework to enable automatic exploration with a pretouch sensor to reduce object shape uncertainty before grasping. The robot starts with only the incomplete object shape data it acquires from a Kinect depth sensor-it has no model of the object. Combining the Kinect pointcloud with prior probability distributions for occlusion and transparency, it makes inferences about portions of the object that the Kinect was not able to observe. Operating on the inferred shape of the object, an iterative grasp replanning and exploration algorithm decides when further exploration is required, and where to explore in the scene using the pretouch sensor. The information gathered by the exploration action is added directly to the robot's environment representation and is considered automatically in the next grasp planning iteration. The pretouch sensor used is the first "seashell effect" pretouch sensor fully integrated into a robot (the PR2); the sensor replaces the PR2's touch sensor pads, and uses the PR2's existing hardware interfaces to eliminate all external wires for power and signal. This new non-contact sensor enables the robot to gather local object shape information without disturbing it. The new sensor and framework was validated on the PR2 robot for its usefulness in sensor-driven grasping of un-modeled objects.