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

Learning to Represent Haptic Feedback for Partially-Observable Tasks

The sense of touch, being the earliest sensory system to develop in a human body [1], plays a critical part of our daily interaction with the environment. In order to successfully complete a task, many manipulation interactions require incorporating haptic feedback. However, manually designing a feedback mechanism can be extremely challenging. In this work, we consider manipulation tasks that need to incorporate tactile sensor feedback in order to modify a provided nominal plan. To incorporate partial observation, we present a new framework that models the task as a partially observable Markov decision process (POMDP) and learns an appropriate representation of haptic feedback which can serve as the state for a POMDP model. The model, that is parametrized by deep recurrent neural networks, utilizes variational Bayes methods to optimize the approximate posterior. Finally, we build on deep Q-learning to be able to select the optimal action in each state without access to a simulator. We test our model on a PR2 robot for multiple tasks of turning a knob until it clicks.Comment: IEEE International Conference on Robotics and Automation (ICRA), 201

Learning from sensory predictions for autonomous and adaptive exploration of object shape with a tactile robot

Humans use information from sensory predictions, together with current observations, for the optimal exploration and recognition of their surrounding environment. In this work, two novel adaptive perception strategies are proposed for accurate and fast exploration of object shape with a robotic tactile sensor. These strategies called (1) adaptive weighted prior and (2) adaptive weighted posterior, combine tactile sensory predictions and current sensor observations to autonomously adapt the accuracy and speed of active Bayesian perception in object exploration tasks. Sensory predictions, obtained from a forward model, use a novel Predicted Information Gain method. These predictions are used by the tactile robot to analyse ‘what would have happened’ if certain decisions ‘would have been made’ at previous decision times. The accuracy of predictions is evaluated and controlled by a confidence parameter, to ensure that the adaptive perception strategies rely more on predictions when they are accurate, and more on current sensory observations otherwise. This work is systematically validated with the recognition of angle and position data extracted from the exploration of object shape, using a biomimetic tactile sensor and a robotic platform. The exploration task implements the contour following procedure used by humans to extract object shape with the sense of touch. The validation process is performed with the adaptive weighted strategies and active perception alone. The adaptive approach achieved higher angle accuracy (2.8 deg) over active perception (5 deg). The position accuracy was similar for all perception methods (0.18 mm). The reaction time or number of tactile contacts, needed by the tactile robot to make a decision, was improved by the adaptive perception (1 tap) over active perception (5 taps). The results show that the adaptive perception strategies can enable future robots to adapt their performance, while improving the trade-off between accuracy and reaction time, for tactile exploration, interaction and recognition tasks

Learning from sensory predictions for autonomous and adaptive exploration of object shape with a tactile robot

Humans use information from sensory predictions, together withcurrent observations, for the optimal exploration and recognition oftheir surrounding environment. In this work, two novel adaptiveperception strategies are proposed for accurate and fast exploration ofobject shape with a robotic tactile sensor. These strategies called 1)adaptive weighted prior and 2) adaptive weighted posterior, combinetactile sensory predictions and current sensor observations toautonomously adapt the accuracy and speed of active Bayesian perceptionin object exploration tasks. Sensory predictions, obtained from a forwardmodel, use a novel Predicted Information Gain method. These predictionsare used by the tactile robot to analyse `what would have happened' ifcertain decisions `would have been made' at previous decision times. Theaccuracy of predictions is evaluated and controlled by a confidenceparameter, to ensure that the adaptive perception strategies rely more onpredictions when they are accurate, and more on current sensoryobservations otherwise. This work is systematically validated with therecognition of angle and position data extracted from the exploration ofobject shape, using a biomimetic tactile sensor and a robotic platform.The exploration task implements the contour following procedure used byhumans to extract object shape with the sense of touch. The validationprocess is performed with the adaptive weighted strategies and activeperception alone. The adaptive approach achieved higher angle accuracy(2.8 deg) over active perception (5 deg). The position accuracy wassimilar for all perception methods (0.18 mm). The reaction time or numberof tactile contacts, needed by the tactile robot to make a decision, wasimproved by the adaptive perception (1 tap) over active perception (5taps). The results show that the adaptive perception strategies canenable future robots to adapt their performance, while improving thetrade-off between accuracy and reaction time, for tactile exploration,interaction and recognition tasks

Multimodal Hierarchical Dirichlet Process-based Active Perception

In this paper, we propose an active perception method for recognizing object categories based on the multimodal hierarchical Dirichlet process (MHDP). The MHDP enables a robot to form object categories using multimodal information, e.g., visual, auditory, and haptic information, which can be observed by performing actions on an object. However, performing many actions on a target object requires a long time. In a real-time scenario, i.e., when the time is limited, the robot has to determine the set of actions that is most effective for recognizing a target object. We propose an MHDP-based active perception method that uses the information gain (IG) maximization criterion and lazy greedy algorithm. We show that the IG maximization criterion is optimal in the sense that the criterion is equivalent to a minimization of the expected Kullback--Leibler divergence between a final recognition state and the recognition state after the next set of actions. However, a straightforward calculation of IG is practically impossible. Therefore, we derive an efficient Monte Carlo approximation method for IG by making use of a property of the MHDP. We also show that the IG has submodular and non-decreasing properties as a set function because of the structure of the graphical model of the MHDP. Therefore, the IG maximization problem is reduced to a submodular maximization problem. This means that greedy and lazy greedy algorithms are effective and have a theoretical justification for their performance. We conducted an experiment using an upper-torso humanoid robot and a second one using synthetic data. The experimental results show that the method enables the robot to select a set of actions that allow it to recognize target objects quickly and accurately. The results support our theoretical outcomes.Comment: submitte