Use of Slip Prediction for Learning Grasp-Stability Policies in Robotic-Grasp Simulation

The purpose of prosthetic hands is to restore a portion of dexterity lost through upper limb amputation. However, a key capability of human grasping that is missing from most currently available prosthetic hands is the ability to adapt grasp forces in response to slip or disturbances without visual information. Current prosthetic hands do not have the integrated tactile sensors or control policies to support adaptive grasp stabilization or manipulation. Research on slip detection and classification has been providing a pathway towards integrating tactile sensors on robotic and prosthetic hands; however, current literature focuses on specific sensors and simple graspers. Policies that use slip prediction to adapt grasp forces are still largely unexplored. Rigid-body simulations have recently emerged as a useful tool for training control policies due to improvements in machine learning techniques. Simulations allow large amounts of interactive data to be generated for training. However, since simulations only approximate reality, policies trained in simulation may not be transferable to physical systems. Several grasp policies with impressive dexterity have been trained in simulation and transferred successfully to physical systems. However, these grasp policies used visual data as policy inputs instead of tactile data. This research investigates if rigid-body simulations can use slip prediction as the primary input for training grasp stabilization policies. Since current slip detection and prediction literature is based on specific tactile sensors and grasper setups, testing slip-reactive grasp policies is difficult, especially with an anthropomorphic hand. As an alternative to implementing a system-specific policy, real human grasp poses and motion-trajectories were used to test if the trained policy could replicate known human grasp stability. To acquire the human grasp data, grasp and motion trajectories from a human motion-capture dataset were adapted into a simulation. Since motion-capture only includes grasp and object pose data, grasp forces had to be inferred through a combination of analytical and iterative methods. Simulation contacts are also just approximate models; therefore, slip in the simulation was characterized for detection and prediction. The stability of the converted grasps was tested by simulating the grasp manipulation episodes with no control policy. Viable grasps were expected to maintain stability until the manipulation trajectory caused grasp degradation or loss. The initial grasps maintained stability for an average of 27.7% of the grasp episode durations, though with a wide standard deviation of 35%. The large standard deviation is due to episodes with high hand acceleration trajectories, as well as grasp objects with varying grasping difficulty. Policy training using the imported grasps and trajectories was performed using reinforcement learning, specifically proximal-policy optimization. Policies were trained with and without slip prediction inputs, using different reward functions: a reward proportional to the duration of grasp stability, and a reward that also added a grasp-force magnitude penalty. A multi-layer perceptron was used as the policy function approximator. The policies without slip-prediction inputs did not converge, while the policy with slip inputs and the grasp-force penalty-reward function converged on a poorly performing policy. On average, episodes tested with the policy that used a grasp-force-penalty had a 0.11 s reduction in grasp stability duration compared to the initial grasp duration results. However, episodes that did have improved stability under the learned policy improved on average by 0.38 s, significantly higher than the average stability loss. Moreover, the change in stability duration under the trained policy negatively correlated with the initial stability duration (Pearson -0.69, p-value 9.79e-11). These results suggest that slip predictions contribute to learned grasp policies, and that reward shaping is critical to the grasp-stability task. Ultimately, the trained policies did not perform better than the baseline no-policy grasp stability, suggesting that the slip predictions were not sufficient to train reasonable grasp policies in simulation

TactileGCN: A Graph Convolutional Network for Predicting Grasp Stability with Tactile Sensors

Tactile sensors provide useful contact data during the interaction with an object which can be used to accurately learn to determine the stability of a grasp. Most of the works in the literature represented tactile readings as plain feature vectors or matrix-like tactile images, using them to train machine learning models. In this work, we explore an alternative way of exploiting tactile information to predict grasp stability by leveraging graph-like representations of tactile data, which preserve the actual spatial arrangement of the sensor's taxels and their locality. In experimentation, we trained a Graph Neural Network to binary classify grasps as stable or slippery ones. To train such network and prove its predictive capabilities for the problem at hand, we captured a novel dataset of approximately 5000 three-fingered grasps across 41 objects for training and 1000 grasps with 10 unknown objects for testing. Our experiments prove that this novel approach can be effectively used to predict grasp stability

More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch

For humans, the process of grasping an object relies heavily on rich tactile feedback. Most recent robotic grasping work, however, has been based only on visual input, and thus cannot easily benefit from feedback after initiating contact. In this paper, we investigate how a robot can learn to use tactile information to iteratively and efficiently adjust its grasp. To this end, we propose an end-to-end action-conditional model that learns regrasping policies from raw visuo-tactile data. This model -- a deep, multimodal convolutional network -- predicts the outcome of a candidate grasp adjustment, and then executes a grasp by iteratively selecting the most promising actions. Our approach requires neither calibration of the tactile sensors, nor any analytical modeling of contact forces, thus reducing the engineering effort required to obtain efficient grasping policies. We train our model with data from about 6,450 grasping trials on a two-finger gripper equipped with GelSight high-resolution tactile sensors on each finger. Across extensive experiments, our approach outperforms a variety of baselines at (i) estimating grasp adjustment outcomes, (ii) selecting efficient grasp adjustments for quick grasping, and (iii) reducing the amount of force applied at the fingers, while maintaining competitive performance. Finally, we study the choices made by our model and show that it has successfully acquired useful and interpretable grasping behaviors.Comment: 8 pages. Published on IEEE Robotics and Automation Letters (RAL). Website: https://sites.google.com/view/more-than-a-feelin

Grasp Stability Prediction for a Dexterous Robotic Hand Combining Depth Vision and Haptic Bayesian Exploration.

Grasp stability prediction of unknown objects is crucial to enable autonomous robotic manipulation in an unstructured environment. Even if prior information about the object is available, real-time local exploration might be necessary to mitigate object modelling inaccuracies. This paper presents an approach to predict safe grasps of unknown objects using depth vision and a dexterous robot hand equipped with tactile feedback. Our approach does not assume any prior knowledge about the objects. First, an object pose estimation is obtained from RGB-D sensing; then, the object is explored haptically to maximise a given grasp metric. We compare two probabilistic methods (i.e. standard and unscented Bayesian Optimisation) against random exploration (i.e. uniform grid search). Our experimental results demonstrate that these probabilistic methods can provide confident predictions after a limited number of exploratory observations, and that unscented Bayesian Optimisation can find safer grasps, taking into account the uncertainty in robot sensing and grasp execution