Category Archives: Robotics

A robot architecture for humanoids able to coordinate different cognitive processes (perception, decision-making, etc.) in a hierarchical fashion

June 14, 2018 08:59 ,

J. Hwang and J. Tani, Seamless Integration and Coordination of Cognitive Skills in Humanoid Robots: A Deep Learning Approach, IEEE Transactions on Cognitive and Developmental Systems, vol. 10, no. 2, pp. 345-358 DOI: 10.1109/TCDS.2017.2714170.

This paper investigates how adequate coordination among the different cognitive processes of a humanoid robot can be developed through end-to-end learning of direct perception of visuomotor stream. We propose a deep dynamic neural network model built on a dynamic vision network, a motor generation network, and a higher-level network. The proposed model was designed to process and to integrate direct perception of dynamic visuomotor patterns in a hierarchical model characterized by different spatial and temporal constraints imposed on each level. We conducted synthetic robotic experiments in which a robot learned to read human’s intention through observing the gestures and then to generate the corresponding goal-directed actions. Results verify that the proposed model is able to learn the tutored skills and to generalize them to novel situations. The model showed synergic coordination of perception, action, and decision making, and it integrated and coordinated a set of cognitive skills including visual perception, intention reading, attention switching, working memory, action preparation, and execution in a seamless manner. Analysis reveals that coherent internal representations emerged at each level of the hierarchy. Higher-level representation reflecting actional intention developed by means of continuous integration of the lower-level visuo-proprioceptive stream.

Posted in: Developmental robotics , Tagged: , ,

An interesting model of Basal Ganglia that performs similarly to Q learning when applied to a robot

June 14, 2018 08:55 ,

Y. Zeng, G. Wang and B. Xu, A Basal Ganglia Network Centric Reinforcement Learning Model and Its Application in Unmanned Aerial Vehicle, IEEE Transactions on Cognitive and Developmental Systems, vol. 10, no. 2, pp. 290-303 DOI: 10.1109/TCDS.2017.2649564.

Reinforcement learning brings flexibility and generality for machine learning, while most of them are mathematical optimization driven approaches, and lack of cognitive and neural evidence. In order to provide a more cognitive and neural mechanisms driven foundation and validate its applicability in complex task, we develop a basal ganglia (BG) network centric reinforcement learning model. Compared to existing work on modeling BG, this paper is unique from the following perspectives: 1) the orbitofrontal cortex (OFC) is taken into consideration. OFC is critical in decision making because of its responsibility for reward representation and is critical in controlling the learning process, while most of the BG centric models do not include OFC; 2) to compensate the inaccurate memory of numeric values, precise encoding is proposed to enable working memory system remember important values during the learning process. The method combines vector convolution and the idea of storage by digit bit and is efficient for accurate value storage; and 3) for information coding, the Hodgkin-Huxley model is used to obtain a more biological plausible description of action potential with plenty of ionic activities. To validate the effectiveness of the proposed model, we apply the model to the unmanned aerial vehicle (UAV) autonomous learning process in a 3-D environment. Experimental results show that our model is able to give the UAV the ability of free exploration in the environment and has comparable learning speed as the Q learning algorithm, while the major advances for our model is that it is with solid cognitive and neural basis.

Posted in: Applications of reinforcement learning to robots, Psycho-physiological bases of engineering , Tagged:

Interesting study about how to quantify the uncertainty in SLAM and the preservation of its monotonic growth, which is needed to good decision making in active SLAM

June 14, 2018 08:48 ,

M. L. Rodríguez-Arévalo, J. Neira and J. A. Castellanos, On the Importance of Uncertainty Representation in Active SLAM, IEEE Transactions on Robotics, vol. 34, no. 3, pp. 829-834 DOI: 10.1109/TRO.2018.2808902.

The purpose of this work is to highlight the paramount importance of representing and quantifying uncertainty to correctly report the associated confidence of the robot’s location estimate at each time step along its trajectory and therefore decide the correct course of action in an active SLAM mission. We analyze the monotonicity property of different decision-making criteria, both in 2-D and 3-D, with respect to the representation of uncertainty and of the orientation of the robot’s pose. Monotonicity, the property that uncertainty increases as the robot moves, is essential for adequate decision making. We analytically show that, by using differential representations to propagate spatial uncertainties, monotonicity is preserved for all optimality criteria, A-opt, D-opt, and E-opt, and for Shannon’s entropy. We also show that monotonicity does not hold for any criteria in absolute representations using Roll-Pitch-Yaw and Euler angles. Finally, using unit quaternions in absolute representations, the only criteria that preserve monotonicity are D-opt and Shannon’s entropy.

Posted in: Mobile robot SLAM , Tagged:

Evaluating the safeness of a motion plan for mobile robot navigation

June 12, 2018 07:54 ,

Brian Axelrod, Leslie Pack Kaelbling, and Tomás Lozano-Pérez Provably safe robot navigation with obstacle uncertainty, The International Journal of Robotics Research Vol 37, Issue 7 DOI: 10.1177/0278364918778338.

As drones and autonomous cars become more widespread, it is becoming increasingly important that robots can operate safely under realistic conditions. The noisy information fed into real systems means that robots must use estimates of the environment to plan navigation. Efficiently guaranteeing that the resulting motion plans are safe under these circumstances has proved difficult. We examine how to guarantee that a trajectory or policy has at most ϵ collision probability (ϵ-safe) with only imperfect observations of the environment. We examine the implications of various mathematical formalisms of safety and arrive at a mathematical notion of safety of a long-term execution, even when conditioned on observational information. We explore the idea of shadows that generalize the notion of a confidence set to estimated shapes and present a theorem that allows us to understand the relationship between shadows and their classical statistical equivalents such as confidence and credible sets. We present efficient algorithms that use shadows to prove that trajectories or policies are safe with much tighter bounds than in previous work. Notably, the complexity of the environment does not affect our method’s ability to evaluate whether a trajectory or policy is safe. We then use these safety-checking methods to design a safe variant of the rapidly exploring random tree (RRT) planning algorithm.

Posted in: Robot motion planning , Tagged:

Shared autonomy where the target is predicted with POMDPs to cope with uncertain predictions

June 12, 2018 07:52 ,

Shervin Javdani, Henny Admoni, Stefania Pellegrinelli, Siddhartha S. Srinivasa, and J. Andrew Bagnell Shared autonomy via hindsight optimization for teleoperation and teaming, The International Journal of Robotics Research Vol 37, Issue 7, pp. 717 – 742 DOI: 10.1177/0278364918776060.

In shared autonomy, a user and autonomous system work together to achieve shared goals. To collaborate effectively, the autonomous system must know the user’s goal. As such, most prior works follow a predict-then-act model, first predicting the user’s goal with high confidence, then assisting given that goal. Unfortunately, confidently predicting the user’s goal may not be possible until they have nearly achieved it, causing predict-then-act methods to provide little assistance. However, the system can often provide useful assistance even when confidence for any single goal is low (e.g. move towards multiple goals). In this work, we formalize this insight by modeling shared autonomy as a partially observable Markov decision process (POMDP), providing assistance that minimizes the expected cost-to-go with an unknown goal. As solving this POMDP optimally is intractable, we use hindsight optimization to approximate. We apply our framework to both shared-control teleoperation and human–robot teaming. Compared with predict-then-act methods, our method achieves goals faster, requires less user input, decreases user idling time, and results in fewer user–robot collisions.

Posted in: Human teleoperation , Tagged: ,

A new mathematical formulation of manipulator motion that simplifies dynamics and kinematics

May 28, 2018 08:08 ,

Labbé, M. & Michaud, F., Comprehensive theory of differential kinematics and dynamics towards extensive motion optimization framework, The International Journal of Robotics Research First Published May 20, 2018 DOI: 10.1177/0278364918772893.

This paper presents a novel unified theoretical framework for differential kinematics and dynamics for the optimization of complex robot motion. By introducing an 18×18 comprehensive motion transformation matrix, the forward differential kinematics and dynamics, including velocity and acceleration, can be written in a simple chain product similar to an ordinary rotational matrix. This formulation enables the analytical computation of derivatives of various physical quantities (e.g. link velocities, link accelerations, or joint torques) with respect to joint coordinates, velocities and accelerations for a robot trajectory in an efficient manner (O(NJ), where NJ is the number of the robot’s degree of freedom), which is useful for motion optimization. Practical implementation of gradient computation is demonstrated together with simulation results of robot motion optimization to validate the effectiveness of the proposed framework.

Posted in: Industrial robots , Tagged: ,

Using short- and long-term memories in SLAM

May 26, 2018 08:00 ,

Labbé, M. & Michaud, F., Long-term online multi-session graph-based SPLAM with memory management, Auton Robot (2018) 42: 1133. DOI: 10.1007/s10514-017-9682-5.

For long-term simultaneous planning, localization and mapping (SPLAM), a robot should be able to continuously update its map according to the dynamic changes of the environment and the new areas explored. With limited onboard computation capabilities, a robot should also be able to limit the size of the map used for online localization and mapping. This paper addresses these challenges using a memory management mechanism, which identifies locations that should remain in a Working Memory (WM) for online processing from locations that should be transferred to a Long-Term Memory (LTM). When revisiting previously mapped areas that are in LTM, the mechanism can retrieve these locations and place them back in WM for online SPLAM. The approach is tested on a robot equipped with a short-range laser rangefinder and a RGB-D camera, patrolling autonomously 10.5 km in an indoor environment over 11 sessions while having encountered 139 people.

Posted in: Mobile robot SLAM , Tagged:

A novel approach to avoid the minima problem in potential fields navigation

May 22, 2018 07:56 ,

Fedele, G., D’Alfonso, L., Chiaravalloti, F. et al., Obstacles Avoidance Based on Switching Potential Functions, J Intell Robot Syst (2018) 90: 387. DOI: 10.1007/s10846-017-0687-2.

In this paper, a novel path planning and obstacles avoidance method for a mobile robot is proposed. This method makes use of a switching strategy between the attractive potential of the target and a new helicoidal potential field which allows to bypass an obstacle by driving the robot around it. The new technique aims at overcoming the local minima problems of the well-known artificial potentials method, caused by the summation of two (or more) potential fields. In fact, in the proposed approach, only a single potential is used at a time. The resulting proposed technique uses only local information and ensures high robustness, in terms of achieved performance and computational complexity, w.r.t. the number of obstacles. Numerical simulations, together with comparisons with existing methods, confirm a very robust behavior of the method, also in the case of a framework with multiple obstacles.

Posted in: Robot motion planning , Tagged: ,

Faster long-term SLAM through direct use of Lie groups in filtering

May 21, 2018 07:52 ,

Kruno Lenac, Josip Ćesić, Ivan Marković, and Ivan Petrović, Exactly sparse delayed state filter on Lie groups for long-term pose graph SLAM, The International Journal of Robotics Research Vol 37, Issue 6, pp. 585 – 610 DOI: 10.1177/0278364918767756.

In this paper we propose a simultaneous localization and mapping (SLAM) back-end solution called the exactly sparse delayed state filter on Lie groups (LG-ESDSF). We derive LG-ESDSF and demonstrate that it retains all the good characteristics of the classic Euclidean ESDSF, the main advantage being the exact sparsity of the information matrix. The key advantage of LG-ESDSF in comparison with the classic ESDSF lies in the ability to respect the state space geometry by negotiating uncertainties and employing filtering equations directly on Lie groups. We also exploit the special structure of the information matrix in order to allow long-term operation while the robot is moving repeatedly through the same environment. To prove the effectiveness of the proposed SLAM solution, we conducted extensive experiments on two different publicly available datasets, namely the KITTI and EuRoC datasets, using two front-ends: one based on the stereo camera and the other on the 3D LIDAR. We compare LG-ESDSF with the general graph optimization framework (g2o) when coupled with the same front-ends. Similarly to g2o the proposed LG-ESDSF is front-end agnostic and the comparison demonstrates that our solution can match the accuracy of g2o, while maintaining faster computation times. Furthermore, the proposed back-end coupled with the stereo camera front-end forms a complete visual SLAM solution dubbed LG-SLAM. Finally, we evaluated LG-SLAM using the online KITTI protocol and at the time of writing it achieved the second best result among the stereo odometry solutions and the best result among the tested SLAM algorithms.

Posted in: Mobile robot SLAM , Tagged: , ,

Shared autonomy in robot teleoperation where the robot learns the user’s skills to adapt to them

May 11, 2018 08:28 ,

Enayati, N., Ferrigno, G. & De Momi, E. Real time implementation of socially acceptable collision avoidance of a low speed autonomous shuttle using the elastic band method, Auton Robot (2018) 42: 997, DOI: 10.1007/s10514-017-9675-4.

This work proposes a shared-control tele-operation framework that adapts its cooperative properties to the estimated skill level of the operator. It is hypothesized that different aspects of an operator’s performance in executing a tele-operated path tracking task can be assessed through conventional machine learning methods using motion-based and task-related features. To identify performance measures that capture motor skills linked to the studied task, an experiment is conducted where users new to tele-operation, practice towards motor skill proficiency in 7 training sessions. A set of classifiers are then learned from the acquired data and selected features, which can generate a skill profile that comprises estimations of user’s various competences. Skill profiles are exploited to modify the behavior of the assistive robotic system accordingly with the objective of enhancing user experience by preventing unnecessary restriction for skilled users. A second experiment is implemented in which novice and expert users execute the path tracking on different pathways while being assisted by the robot according to their estimated skill profiles. Results validate the skill estimation method and hint at feasibility of shared-control customization in tele-operated path tracking.

Posted in: Human teleoperation , Tagged: