Active End-Effector Pose Selection for Tactile Object Recognition through Monte Carlo Tree Search

This paper considers the problem of active object recognition using touch only. The focus is on adaptively selecting a sequence of wrist poses that achieves accurate recognition by enclosure grasps. It seeks to minimize the number of touches and maximize recognition confidence. The actions are formulated as wrist poses relative to each other, making the algorithm independent of absolute workspace coordinates. The optimal sequence is approximated by Monte Carlo tree search. We demonstrate results in a physics engine and on a real robot. In the physics engine, most object instances were recognized in at most 16 grasps. On a real robot, our method recognized objects in 2--9 grasps and outperformed a greedy baseline.Comment: Accepted to International Conference on Intelligent Robots and Systems (IROS) 201

Active End-Effector Pose Selection for Tactile Object Recognition through Monte Carlo Tree Search

This paper considers the problem of active object recognition using touch only. The focus is on adaptively selecting a sequence of wrist poses that achieves accurate recognition by enclosure grasps. It seeks to minimize the number of touches and maximize recognition confidence. The actions are formulated as wrist poses relative to each other, making the algorithm independent of absolute workspace coordinates. The optimal sequence is approximated by Monte Carlo tree search. We demonstrate results in a physics engine and on a real robot. In the physics engine, most object instances were recognized in at most 16 grasps. On a real robot, our method recognized objects in 2--9 grasps and outperformed a greedy baseline.Comment: Accepted to International Conference on Intelligent Robots and Systems (IROS) 201

TANDEM3D: Active Tactile Exploration for 3D Object Recognition

Tactile recognition of 3D objects remains a challenging task. Compared to 2D shapes, the complex geometry of 3D surfaces requires richer tactile signals, more dexterous actions, and more advanced encoding techniques. In this work, we propose TANDEM3D, a method that applies a co-training framework for exploration and decision making to 3D object recognition with tactile signals. Starting with our previous work, which introduced a co-training paradigm for 2D recognition problems, we introduce a number of advances that enable us to scale up to 3D. TANDEM3D is based on a novel encoder that builds 3D object representation from contact positions and normals using PointNet++. Furthermore, by enabling 6DOF movement, TANDEM3D explores and collects discriminative touch information with high efficiency. Our method is trained entirely in simulation and validated with real-world experiments. Compared to state-of-the-art baselines, TANDEM3D achieves higher accuracy and a lower number of actions in recognizing 3D objects and is also shown to be more robust to different types and amounts of sensor noise. Video is available at https://jxu.ai/tandem3d

Novel Tactile-SIFT Descriptor for Object Shape Recognition

Using a tactile array sensor to recognize an object often requires multiple touches at different positions. This process is prone to move or rotate the object, which inevitably increases difficulty in object recognition. To cope with the unknown object movement, this paper proposes a new tactile-SIFT descriptor to extract features in view of gradients in the tactile image to represent objects, to allow the features being invariant to object translation and rotation. The tactile-SIFT segments a tactile image into overlapping subpatches, each of which is represented using a dn-dimensional gradient vector, similar to the classic SIFT descriptor. Tactile-SIFT descriptors obtained from multiple touches form a dictionary of k words, and the bag-of-words method is then used to identify objects. The proposed method has been validated by classifying 18 real objects with data from an off-the-shelf tactile sensor. The parameters of the tactile-SIFT descriptor, including the dimension size dn and the number of subpatches sp, are studied. It is found that the optimal performance is obtained using an 8-D descriptor with three subpatches, taking both the classification accuracy and time efficiency into consideration. By employing tactile-SIFT, a recognition rate of 91.33% has been achieved with a dictionary size of 50 clusters using only 15 touches

Grasp and stress analysis of an underactuated finger for proprioceptive tactile sensing

This paper presents the design and evaluation of a new sensorized underactuated self-adaptive finger. The design incorporates a two-degrees-of-freedom link-driven underactuated mechanism with an embedded load cell for contact force measurement and a trimmer potentiometer for acquiring joint variables. The utilization of proprioceptive (internal) sensors results in tactile-like sensations in the finger without compromising the size and complexity of the proposed design. To obtain an optimum finger design, the placement of the load cell is analyzed using finite element method. The design of the finger features a particular rounded shape of the distal phalanx and specific size ratio between the phalanxes to enable both precision and power grasps. A quantitative evaluation of the grasp efficiency by constructing a grasp wrench space is provided. The effectiveness of the proposed design is verified through experimental results that demonstrate the grasp external wrench tolerance, shape adaptability, and tactile capability. All CAD files and ROS package for the proposed underactuated design can be found on https://github.com/mahyaret