Technical session talks from ICRA 2012
TechTalks from event: Technical session talks from ICRA 2012
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Applied Machine Learning
Active Learning from Demonstration for Robust Autonomous NavigationBuilding robust and reliable autonomous navigation systems that generalize across environments and operating scenarios remains a core challenge in robotics. Machine learning has proven a significant aid in this task; in recent years learning from demonstration has become especially popular, leading to improved systems while requiring less expert tuning and interaction. However, these approaches still place a burden on the expert, specifically to choose the best demonstrations to provide. This work proposes two approaches for active learning from demonstration, in which the learning system requests specific demonstrations from the expert. The approaches identify examples for which expert demonstration is predicted to provide useful information on concepts which are either novel or uncertain to the current system. Experimental results demonstrate both improved generalization performance and reduced expert interaction when using these approaches.
Tendon-Driven Control of Biomechanical and Robotic Systems: A Path Integral Reinforcement Learning ApproachWe apply path integral reinforcement learning to a biomechanically accurate dynamics model of the index finger and then to the Anatomically Correct Testbed (ACT) robotic hand. We illustrate the applicability of Policy Improvement with Path Integrals to parameterized and non-parameterized control policies. This method is based on sampling variations in control, executing them in the real world, and minimizing a cost function on the resulting performance. Iteratively improving the control policy based on real-world performance requires no direct modeling of tendon network nonlinearities and contact transitions, allowing improved task performance.
Slip Prediction Using Hidden Markov Models: Multidimensional Sensor Data to Symbolic Temporal Pattern LearningWe present experiments on the application of machine learning to predicting slip. The sensing information is provided by a force/torque sensor and an artificial finger, which has randomly distributed strain gauges and polyvinylidene fluoride (PVDF) films embedded in silicone resulting in multidimensional time series data on the finger-object contact. An incipient slip is detected by studying temporal patterns in the data. The data is analysed using probabilistic clustering that transforms the data into a sequence of symbols, which is used to train a hidden Markov model (HMM) classifier. Experimental results show that the classifier can predict a slip, at least 100ms before a slip takes place, with an accuracy of 96% on the validation set.
Collision-Free State EstimationIn state estimation, we often want the maximum likelihood estimate of the current state. For the commonly used joint multivariate Gaussian distribution over the state space, this can be efficiently found using a Kalman filter. However, in complex environments the state space is often highly constrained. For example, for objects within a refrigerator, they cannot interpenetrate each other or the refrigerator walls. The multivariate Gaussian is unconstrained over the state space and cannot incorporate these constraints. In particular, the state estimate returned by the unconstrained distribution may itself be infeasible. Instead, we solve a related constrained optimization problem to find a good feasible state estimate. We illustrate this for estimating collision-free configurations for objects resting stably on a 2-D surface, and demonstrate its utility in a real robot perception domain.
Fault Detection and Isolation from Uninterpreted Data in Robotic Sensorimotor CascadesOne of the challenges in designing the next generation of robots operating in non-engineered environments is that there seems to be an infinite amount of causes that make the sensor data unreliable or actuators ineffective. In this paper, we discuss what faults are possible to detect using zero modeling effort: we start from uninterpreted streams of observations and commands, and without a prior knowledge of a model of the world. We show that in sensorimotor cascades it is possible to define static faults independently of a nominal model. We define an information-theoretic usefulness of a sensor reading and we show that it captures several kind of sensorimotor faults frequently encountered in practice. We particularize these ideas to the case of BDS/BGDS models, proposed in previous work as suitable candidates for describing generic sensorimotor cascades. We show several examples with camera and range-finder data, and we discuss a possible way to integrate these techniques in an existing robot software architecture.
Describing and Classifying Spatial and Temporal Contexts with OWL DL in Ubiquitous RoboticsThe article describes a system for describing and recognizing spatial and temporal patterns of events. The system is based on an ontology described through the Description Logics formalism and implemented in OWL DL. The approach is different from all other works in the literature since the system does not require an external reasoning engine, but relies only on the base mechanism for ontology classification. Experiments performed in two different scenarios are described, i.e., a Smart Home and a mobile robot for autonomous transportation operating within a partially automated building.