Monthly Archives: February 2019

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Models of brain based on artificial neural networks

James C.R. Whittington, Rafal Bogacz, Theories of Error Back-Propagation in the Brain, Trends in Cognitive Sciences, Volume 23, Issue 3, 2019, Pages 235-250 DOI: 10.1016/j.tics.2018.12.005.

This review article summarises recently proposed theories on how neural circuits in the brain could approximate the error back-propagation algorithm used by artificial neural networks. Computational models implementing these theories achieve learning as efficient as artificial neural networks, but they use simple synaptic plasticity rules based on activity of presynaptic and postsynaptic neurons. The models have similarities, such as including both feedforward and feedback connections, allowing information about error to propagate throughout the network. Furthermore, they incorporate experimental evidence on neural connectivity, responses, and plasticity. These models provide insights on how brain networks might be organised such that modification of synaptic weights on multiple levels of cortical hierarchy leads to improved performance on tasks.

Model-based RL for controling a soft manipulator arm

T. G. Thuruthel, E. Falotico, F. Renda and C. Laschi, Model-Based Reinforcement Learning for Closed-Loop Dynamic Control of Soft Robotic Manipulators, IEEE Transactions on Robotics, vol. 35, no. 1, pp. 124-134, Feb. 2019. DOI: 10.1109/TRO.2018.2878318.

Dynamic control of soft robotic manipulators is an open problem yet to be well explored and analyzed. Most of the current applications of soft robotic manipulators utilize static or quasi-dynamic controllers based on kinematic models or linearity in the joint space. However, such approaches are not truly exploiting the rich dynamics of a soft-bodied system. In this paper, we present a model-based policy learning algorithm for closed-loop predictive control of a soft robotic manipulator. The forward dynamic model is represented using a recurrent neural network. The closed-loop policy is derived using trajectory optimization and supervised learning. The approach is verified first on a simulated piecewise constant strain model of a cable driven under-actuated soft manipulator. Furthermore, we experimentally demonstrate on a soft pneumatically actuated manipulator how closed-loop control policies can be derived that can accommodate variable frequency control and unmodeled external loads.

Selecting the best visual cues in the next future for reducing the computational cost of localization under limited computational resources

L. Carlone and S. Karaman, Attention and Anticipation in Fast Visual-Inertial Navigation, IEEE Transactions on Robotics, vol. 35, no. 1, pp. 1-20, Feb. 2019 DOI: 10.1109/TRO.2018.2872402.

We study a visual-inertial navigation (VIN) problem in which a robot needs to estimate its state using an on-board camera and an inertial sensor, without any prior knowledge of the external environment. We consider the case in which the robot can allocate limited resources to VIN, due to tight computational constraints. Therefore, we answer the following question: under limited resources, what are the most relevant visual cues to maximize the performance of VIN? Our approach has four key ingredients. First, it is task-driven, in that the selection of the visual cues is guided by a metric quantifying the VIN performance. Second, it exploits the notion of anticipation, since it uses a simplified model for forward-simulation of robot dynamics, predicting the utility of a set of visual cues over a future time horizon. Third, it is efficient and easy to implement, since it leads to a greedy algorithm for the selection of the most relevant visual cues. Fourth, it provides formal performance guarantees: we leverage submodularity to prove that the greedy selection cannot be far from the optimal (combinatorial) selection. Simulations and real experiments on agile drones show that our approach ensures state-of-the-art VIN performance while maintaining a lean processing time. In the easy scenarios, our approach outperforms appearance-based feature selection in terms of localization errors. In the most challenging scenarios, it enables accurate VIN while appearance-based feature selection fails to track robot’s motion during aggressive maneuvers.

An application of MDPs to UAV collision-free navigation with an interesting taxonomy of the state-of-the-art

Xiang Yu1, Xiaobin Zhou2, Youmin Zhang, Collision-Free Trajectory Generation and Tracking for UAVs Using Markov Decision Process in a Cluttered Environment, Journal of Intelligent & Robotic Systems, 2019, 93:17–32 DOI: 10.1007/s10846-018-0802-z.

A collision-free trajectory generation and tracking method capable of re-planning unmanned aerial vehicle (UAV) trajectories can increase flight safety and decrease the possibility of mission failures. In this paper, a Markov decision process (MDP) based algorithm combined with backtracking method is presented to create a safe trajectory in the case of hostile environments. Subsequently, a differential flatness method is adopted to smooth the profile of the rerouted trajectory for satisfying the UAV physical constraints. Lastly, a flight controller based on passivity-based control (PBC) is designed to maintain UAV’s stability and trajectory tracking performance. simulation results demonstrate that the UAV with the proposed strategy is capable of avoiding obstacles in a hostile environment.