Category Archives: Robotics

Survey on Model-Driven Software Engineering for real-time embedded systems and robotics

October 1, 2015 18:37 ,

Brugali, D., Model-Driven Software Engineering in Robotics: Models Are Designed to Use the Relevant Things, Thereby Reducing the Complexity and Cost in the Field of Robotics, in Robotics & Automation Magazine, IEEE , vol.22, no.3, pp.155-166, Sept. 2015, DOI: 10.1109/MRA.2015.2452201.

A model is an abstract representation of a real system or phenomenon [1]. The idea of a model is to capture important properties of reality and to eglect irrelevant details. The properties that are relevant and that can be neglected depend on the purpose of creating a model. A model can make a particular system or phenomenon easier to understand, quantify, visualize, simulate, or predict.

Posted in: Real-Time Systems, Robotic software , Tagged: , ,

Planning tasks in mobile robots with MDPs that maximize the probability of satisfying user’s requirements specified through temporal logics, with estimation of transition probabilities through simulation only when needed

September 30, 2015 18:53 ,

Jing Wang, Xuchu Ding, Morteza Lahijanian, Ioannis Ch. Paschalidis, and Calin A. Belta, Temporal logic motion control using actor–critic methods, The International Journal of Robotics Research September 2015 34: 1329-1344, first published on May 26, 2015. DOI: 10.1177/0278364915581505.

This paper considers the problem of deploying a robot from a specification given as a temporal logic statement about some properties satisfied by the regions of a large, partitioned environment. We assume that the robot has noisy sensors and actuators and model its motion through the regions of the environment as a Markov decision process (MDP). The robot control problem becomes finding the control policy which maximizes the probability of satisfying the temporal logic task on the MDP. For a large environment, obtaining transition probabilities for each state–action pair, as well as solving the necessary optimization problem for the optimal policy, are computationally intensive. To address these issues, we propose an approximate dynamic programming framework based on a least-squares temporal difference learning method of the actor–critic type. This framework operates on sample paths of the robot and optimizes a randomized control policy with respect to a small set of parameters. The transition probabilities are obtained only when needed. Simulations confirm that convergence of the parameters translates to an approximately optimal policy.

Posted in: Robot task planning , Tagged: , ,

Building probabilistic models of physical processes from their deterministic models and some experimental data, with guarantees on the degree of coincidence between the generated model and the real system

September 30, 2015 18:44 ,

Konstantinos Karydis, Ioannis Poulakakis, Jianxin Sun, and Herbert G. Tanner, Probabilistically valid stochastic extensions of deterministic models for systems with uncertainty, The International Journal of Robotics Research September 2015 34: 1278-1295, first published on May 28, 2015. DOI: 10.1177/0278364915576336.

Models capable of capturing and reproducing the variability observed in experimental trials can be valuable for planning and control in the presence of uncertainty. This paper reports on a new data-driven methodology that extends deterministic models to a stochastic regime and offers probabilistic guarantees of model fidelity. From an acceptable deterministic model, a stochastic one is generated, capable of capturing and reproducing uncertain system–environment interactions at given levels of fidelity. The reported approach combines methodological elements from probabilistic model validation and randomized algorithms, to simultaneously quantify the fidelity of a model and tune the distribution of random parameters in the corresponding stochastic extension, in order to reproduce the variability observed experimentally in the physical process of interest. The approach can be applied to an array of physical processes, the models of which may come in different forms, including differential equations; we demonstrate this point by considering examples from the areas of miniature legged robots and aerial vehicles.

Posted in: Probability and statistics, Robot models , Tagged:

Nice related work on efficient POMDPs and two novel approaches to reduce their computational cost

September 28, 2015 12:18 ,

Grady, D.K.; Moll, M.; Kavraki, L.E., Extending the Applicability of POMDP Solutions to Robotic Tasks, in Robotics, IEEE Transactions on , vol.31, no.4, pp.948-961, Aug. 2015 DOI: 10.1109/TRO.2015.2441511

Partially observable Markov decision processes (POMDPs) are used in many robotic task classes from soccer to household chores. Determining an approximately optimal action policy for POMDPs is PSPACE-complete, and the exponential growth of computation time prohibits solving large tasks. This paper describes two techniques to extend the range of robotic tasks that can be solved using a POMDP. Our first technique reduces the motion constraints of a robot and, then, uses state-of-the-art robotic motion planning techniques to respect the true motion constraints at runtime. We then propose a novel task decomposition that can be applied to some indoor robotic tasks. This decomposition transforms a long time horizon task into a set of shorter tasks. We empirically demonstrate the performance gain provided by these two techniques through simulated execution in a variety of environments. Comparing a direct formulation of a POMDP to solving our proposed reductions, we conclude that the techniques proposed in this paper can provide significant enhancement to current POMDP solution techniques, extending the POMDP instances that can be solved to include large continuous-state robotic tasks.

Posted in: Robot task planning , Tagged: ,

A very interesting review of current approaches to SLAM based on smoothing (i.e., graph optimization) and in clustering the map into submaps

July 22, 2015 09:37 ,

Jiantong Cheng, Jonghyuk Kim, Jinliang Shao, Weihua Zhang, Robust linear pose graph-based SLAM, Robotics and Autonomous Systems, Volume 72, October 2015, Pages 71-82, ISSN 0921-8890, DOI: 10.1016/j.robot.2015.04.010.

This paper addresses a robust and efficient solution to eliminate false loop-closures in a pose-graph linear SLAM problem. Linear SLAM was recently demonstrated based on submap joining techniques in which a nonlinear coordinate transformation was performed separately out of the optimization loop, resulting in a convex optimization problem. This however introduces added complexities in dealing with false loop-closures, which mostly stems from two factors: (a) the limited local observations in map-joining stages and (b) the non block-diagonal nature of the information matrix of each submap. To address these problems, we propose a Robust Linear SLAM by (a) developing a delayed optimization for outlier candidates and (b) utilizing a Schur complement to efficiently eliminate corrupted information block. Based on this new strategy, we prove that the spread of outlier information does not compromise the optimization performance of inliers and can be fully filtered out from the corrupted information matrix. Experimental results based on public synthetic and real-world datasets in 2D and 3D environments show that this robust approach can cope with the incorrect loop-closures robustly and effectively.

Posted in: Mobile robot SLAM , Tagged: , , ,

Semantic and syntactic bootstrapped learning for robots, inspired in similar processes in humans, that use language as a scaffolding mechanism to improve learning in unknown situations

July 21, 2015 11:01 ,

Worgotter, F.; Geib, C.; Tamosiunaite, M.; Aksoy, E.E.; Piater, J.; Hanchen Xiong; Ude, A.; Nemec, B.; Kraft, D.; Kruger, N.; Wachter, M.; Asfour, T., Structural Bootstrapping—A Novel, Generative Mechanism for Faster and More Efficient Acquisition of Action-Knowledge, Autonomous Mental Development, IEEE Transactions on , vol.7, no.2, pp.140,154, June 2015, DOI: 10.1109/TAMD.2015.2427233.

Humans, but also robots, learn to improve their behavior. Without existing knowledge, learning either needs to be explorative and, thus, slow or-to be more efficient-it needs to rely on supervision, which may not always be available. However, once some knowledge base exists an agent can make use of it to improve learning efficiency and speed. This happens for our children at the age of around three when they very quickly begin to assimilate new information by making guided guesses how this fits to their prior knowledge. This is a very efficient generative learning mechanism in the sense that the existing knowledge is generalized into as-yet unexplored, novel domains. So far generative learning has not been employed for robots and robot learning remains to be a slow and tedious process. The goal of the current study is to devise for the first time a general framework for a generative process that will improve learning and which can be applied at all different levels of the robot’s cognitive architecture. To this end, we introduce the concept of structural bootstrapping-borrowed and modified from child language acquisition-to define a probabilistic process that uses existing knowledge together with new observations to supplement our robot’s data-base with missing information about planning-, object-, as well as, action-relevant entities. In a kitchen scenario, we use the example of making batter by pouring and mixing two components and show that the agent can efficiently acquire new knowledge about planning operators, objects as well as required motor pattern for stirring by structural bootstrapping. Some benchmarks are shown, too, that demonstrate how structural bootstrapping improves performance.

Posted in: Developmental robotics, Psycho-physiological bases of engineering , Tagged: , ,

Developmental approach for a robot manipulator that learns in several bootstrapped stages, strongly inspired in infant development

July 21, 2015 10:32 ,

Ugur, E.; Nagai, Y.; Sahin, E.; Oztop, E., Staged Development of Robot Skills: Behavior Formation, Affordance Learning and Imitation with Motionese, Autonomous Mental Development, IEEE Transactions on , vol.7, no.2, pp.119,139, June 2015, DOI: 10.1109/TAMD.2015.2426192.

Inspired by infant development, we propose a three staged developmental framework for an anthropomorphic robot manipulator. In the first stage, the robot is initialized with a basic reach-and- enclose-on-contact movement capability, and discovers a set of behavior primitives by exploring its movement parameter space. In the next stage, the robot exercises the discovered behaviors on different objects, and learns the caused effects; effectively building a library of affordances and associated predictors. Finally, in the third stage, the learned structures and predictors are used to bootstrap complex imitation and action learning with the help of a cooperative tutor. The main contribution of this paper is the realization of an integrated developmental system where the structures emerging from the sensorimotor experience of an interacting real robot are used as the sole building blocks of the subsequent stages that generate increasingly more complex cognitive capabilities. The proposed framework includes a number of common features with infant sensorimotor development. Furthermore, the findings obtained from the self-exploration and motionese guided human-robot interaction experiments allow us to reason about the underlying mechanisms of simple-to-complex sensorimotor skill progression in human infants.

Posted in: Developmental robotics, Psycho-physiological bases of engineering , Tagged: , , , , ,

Efficient sampling of the agent-world interaction in reinforcement learning through the use of simulators with diverse fidelity to the real system

July 17, 2015 15:01 ,

Cutler, M.; Walsh, T.J.; How, J.P., Real-World Reinforcement Learning via Multifidelity Simulators, Robotics, IEEE Transactions on , vol.31, no.3, pp.655,671, June 2015, DOI: 10.1109/TRO.2015.2419431.

Reinforcement learning (RL) can be a tool for designing policies and controllers for robotic systems. However, the cost of real-world samples remains prohibitive as many RL algorithms require a large number of samples before learning useful policies. Simulators are one way to decrease the number of required real-world samples, but imperfect models make deciding when and how to trust samples from a simulator difficult. We present a framework for efficient RL in a scenario where multiple simulators of a target task are available, each with varying levels of fidelity. The framework is designed to limit the number of samples used in each successively higher-fidelity/cost simulator by allowing a learning agent to choose to run trajectories at the lowest level simulator that will still provide it with useful information. Theoretical proofs of the framework’s sample complexity are given and empirical results are demonstrated on a remote-controlled car with multiple simulators. The approach enables RL algorithms to find near-optimal policies in a physical robot domain with fewer expensive real-world samples than previous transfer approaches or learning without simulators.

Posted in: Applications of reinforcement learning to robots , Tagged: , , ,

Checking the behavior of robotic software (i.e., verification) and embedded sw in general, with a good related work on the issue

July 17, 2015 12:50 ,

Lyons, D.M.; Arkin, R.C.; Shu Jiang; Tsung-Ming Liu; Nirmal, P., Performance Verification for Behavior-Based Robot Missions, Robotics, IEEE Transactions on , vol.31, no.3, pp.619,636, June 2015, DOI: 10.1109/TRO.2015.2418592.

Certain robot missions need to perform predictably in a physical environment that may have significant uncertainty. One approach is to leverage automatic software verification techniques to establish a performance guarantee. The addition of an environment model and uncertainty in both program and environment, however, means that the state space of a model-checking solution to the problem can be prohibitively large. An approach based on behavior-based controllers in a process-algebra framework that avoids state-space combinatorics is presented here. In this approach, verification of the robot program in the uncertain environment is reduced to a filtering problem for a Bayesian network. Validation results are presented for the verification of a multiple-waypoint and an autonomous exploration robot mission.

Posted in: Robotic software , Tagged: , ,

The problem of monitoring events that can only be predicted stochastically, applied to mobile sensors for monitoring

July 17, 2015 12:18 ,

Jingjin Yu; Karaman, S.; Rus, D., Persistent Monitoring of Events With Stochastic Arrivals at Multiple Stations, Robotics, IEEE Transactions on , vol.31, no.3, pp.521,535, June 2015, DOI: 10.1109/TRO.2015.2409453.

This paper introduces a new mobile sensor scheduling problem involving a single robot tasked to monitor several events of interest that are occurring at different locations (stations). Of particular interest is the monitoring of transient events of a stochastic nature, with applications ranging from natural phenomena (e.g., monitoring abnormal seismic activity around a volcano using a ground robot) to urban activities (e.g., monitoring early formations of traffic congestion using an aerial robot). Motivated by examples like these, this paper focuses on problems in which the precise occurrence times of the events are unknown apriori, but statistics for their interarrival times are available. In monitoring such events, the robot seeks to: (1) maximize the number of events observed and (2) minimize the delay between two consecutive observations of events occurring at the same location. This paper considers the case when a robot is tasked with optimizing the event observations in a balanced manner, following a cyclic patrolling route. To tackle this problem, first, assuming that the cyclic ordering of stations is known, we prove the existence and uniqueness of the optimal solution and show that the solution has desirable convergence rate and robustness. Our constructive proof also yields an efficient algorithm for computing the unique optimal solution with O(n) time complexity, in which n is the number of stations, with O(log n) time complexity for incrementally adding or removing stations. Except for the algorithm, our analysis remains valid when the cyclic order is unknown. We then provide a polynomial-time approximation scheme that computes for any ε > 0 a (1 + ε)-optimal solution for this more general, NP-hard problem.

Posted in: Real-Time Systems, Robot sensors, Task scheduling , Tagged: