Nawid Jamali

A new design of a fingertip for the iCub hand

Tactile sensing is of fundamental importance for object manipulation and perception. Several sensors for hands have been proposed in the literature, however, only a few of them can be fully integrated with robotic hands. Typical problems preventing integration include the need for deformable sensors that can be deployed on curved surfaces, and wiring complexity. In this paper we describe a fingertip for the hands of the iCub robot, each fingertip consists of 12 sensors. Our approach builds on previous work on the iCub tactile system. The sensing elements of the fingertip are capacitive sensors made from a flexible PCB, and a multi-layer fabric that includes the dielectric material and the conductive layer. The novelty the proposed sensor lies in incorporating the multi-layer fabric technology into a small fingertip sensor that can be attached to the hands of a humanoid robot. The new sensors are more robust. The manufacturing is easier and relies on industrial techniques for the fabrication of the components, which results in higher repeatability. We performed experimental characterization of the sensor. We show that the sensor is able to detect forces as low as 0.05 N with no cross-talk between the taxels. We identified some hysteresis in the response of the sensor which must be taken into account if the robot exerts large forces for a long period of time. The taxels have spatially overlapping receptive fields, this has been demonstrated to be a useful property that allows hyperacuity.

Active perception: Building objects' models using tactile exploration

In this paper we present an efficient active learning strategy applied to the problem of tactile exploration of an objects surface. The method uses Gaussian process (GPs) classification to efficiently sample the surface of the object in order to reconstruct its shape. The proposed method iteratively samples the surface of the object, while, simultaneously constructing a probabilistic model of the objects surface. The probabilities in the model are used to guide the exploration. At each iteration, the estimate of the objects shape is used to slice the object in equally spaced intervals along the height of the object. The sampled locations are then labelled according to the interval in which their height falls. In its simple form, the data are labelled as belonging to the object and not belonging to the object: object and no-object, respectively. A GP classifier is trained to learn the object/no-object decision boundary. The next location to be sampled is selected at the classification boundary, in this way, the exploration is biased towards more informative areas. Complex features of the objects surface is captured by increasing the number of intervals as the number of sampled locations is increased. We validated our approach on six objects of different shapes using the iCub humanoid robot. Our experiments show that the method outperforms random selection and previous work based on GP regression by sampling more points on and near-the-boundary of the object.

Controlled tactile exploration and haptic object recognition

In this paper we propose a novel method for in-hand object recognition. The method is composed of a grasp stabilization controller and two exploratory behaviours to capture the shape and the softness of an object. Grasp stabilization plays an important role in recognizing objects. First, it prevents the object from slipping and facilitates the exploration of the object. Second, reaching a stable and repeatable position adds robustness to the learning algorithm and increases invariance with respect to the way in which the robot grasps the object. The stable poses are estimated using a Gaussian mixture model (GMM). We present experimental results showing that using our method the classifier can successfully distinguish 30 objects. We also compare our method with a benchmark experiment, in which the grasp stabilization is disabled. We show, with statistical significance, that our method outperforms the benchmark method.

Event-driven encoding of off-the-shelf tactile sensors for compression and latency optimisation for robotic skin

We propose a method to compress the enormous amount of data originating from tactile sensors is presented that explicitly exploits the inherent sparseness over space and time, sending tactile “events” only when a contact is detected. The resulting modular architecture is based on FPGA modules that acquire data samples from off-the-shelf tactile sensors based on capacitive transducers and generate and transmit an event-driven readout. This architecture has been specifically implemented for integration on robots with a large number of tactile sensors, to reduce communication bandwidth, power and processing requirements. An asynchronous serial address-event representation protocol further optimises effective data transmission rate (efficiency of 94.1%) and latency (340 ns) with respect to more common transmission protocols (e.g., Ethernet, CAN). We propose two complementary algorithms for the translation of raw-data into events, optimising data rate and bandwidth, or exploiting the asynchronous nature of the event-driven encoding and the temporal information within the sensory signal. Data reduction capability can reach up to 20 % of the correspondent clock-based encoding, with limited information loss due to the compression.

Underwater robot-object contact perception using machine learning on force/torque sensor feedback

Autonomous manipulation of objects requires reliable information on robot-object contact state. Underwater environments can adversely affect sensing modalities such as vision, making them unreliable. In this paper we investigate underwater robot-object contact perception between an autonomous underwater vehicle and a T-bar valve using a force/torque sensor and the robots proprioceptive information. We present an approach in which machine learning is used to learn a classifier for different contact states, namely, a contact aligned with the central axis of the valve, an edge contact and no contact. To distinguish between different contact states, the robot performs an exploratory behavior that produces distinct patterns in the force/torque sensor. The sensor output forms a multidimensional time-series. A probabilistic clustering algorithm is used to analyze the time-series. The algorithm dissects the multidimensional time-series into clusters, producing a one-dimensional sequence of symbols. The symbols are used to train a hidden Markov model, which is subsequently used to predict novel contact conditions. We show that the learned classifier can successfully distinguish the three contact states with an accuracy of 72% ± 12 %.

Covariance analysis as a measure of policy robustness

In this paper we propose covariance analysis as a metric for reinforcement learning to improve the robustness of a learned policy. The local optima found during the exploration are analyzed in terms of the total cumulative reward and the local behavior of the system in the neighborhood of the optima. The analysis is performed in the solution space to select a policy that exhibits robustness in uncertain and noisy environments. We demonstrate the utility of the method using our previously developed system where an autonomous underwater vehicle (AUV) has to recover from a thruster failure [1]. When a failure is detected the recovery system is invoked, which uses simulations to learn a new controller that utilizes the remaining functioning thrusters to achieve the goal of the AUV, that is, to reach a target position. In this paper, we use covariance analysis to examine the performance of the top, n, policies output by the previous algorithm. We propose a scoring metric that uses the output of the covariance analysis, the time it takes the AUV to reach the target position and the distance between the target position and the AUVs final position. The top polices are simulated in a noisy environment and evaluated using the proposed scoring metric to analyze the effect of noise on their performance. The policy that exhibits more tolerance to noise is selected. We show experimental results where covariance analysis successfully selects a more robust policy that was ranked lower by the original algorithm.

Robot-object contact perception using symbolic temporal pattern learning

This paper investigates application of machine learning to the problem of contact perception between a robots gripper and an object. The input data comprises a multidimensional time-series produced by a force/torque sensor at the robots wrist, the robots proprioceptive information, namely, the position of the end-effector, as well as the robots control command. These data are used to train a hidden Markov model (HMM) classifier. The output of the classifier is a prediction of the contact state, which includes no contact, a contact aligned with the central axis of the valve, and an edge contact. To distinguish between contact states, the robot performs exploratory behaviors that produce distinct patterns in the time-series data. The patterns are discovered by first analyzing the data using a probabilistic clustering algorithm that transforms the multidimensional data into a one-dimensional sequence of symbols. The symbols produced by the clustering algorithm are used to train the HMM classifier. We examined two exploratory behaviors: a rotation around the x-axis, and a rotation around the y-axis of the gripper. We show that using these two exploratory behaviors we can successfully predict a contact state with an accuracy of 88 ± 5 % and 81 ± 10 %, respectively.