Monthly Archives: September 2023

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On how the exploitation-exploration dicotomy shifts to exploitation as humans get older

R. Nathan Spreng, Gary R. Turner, From exploration to exploitation: a shifting mental mode in late life development, Trends in Cognitive Sciences, Volume 25, Issue 12, 2021 DOI: 10.1016/j.tics.2021.09.0010.

Changes in cognition, affect, and brain function combine to promote a shift in the nature of mentation in older adulthood, favoring exploitation of prior knowledge over exploratory search as the starting point for thought and action. Age-related exploitation biases result from the accumulation of prior knowledge, reduced cognitive control, and a shift toward affective goals. These are accompanied by changes in cortical networks, as well as attention and reward circuits. By incorporating these factors into a unified account, the exploration-to-exploitation shift offers an integrative model of cognitive, affective, and brain aging. Here, we review evidence for this model, identify determinants and consequences, and survey the challenges and opportunities posed by an exploitation-biased mental mode in later life.

Modelling network delay in the remote estimation of the robot state for networked telerobots

Barnali Das, Gordon Dobie, Delay compensated state estimation for Telepresence robot navigation, Robotics and Autonomous Systems, Volume 146, 2021 DOI: 10.1016/j.robot.2021.103890.

Telepresence robots empower human operators to navigate remote environments. However, operating and navigating the robot in an unknown environment is challenging due to delay in the communication network (e.g.,�distance, bandwidth, communication drop-outs etc.), processing delays and slow dynamics of the mobile robots resulting in time-lagged in the system. Also, erroneous sensor data measurement which is important to estimate the robot\u2019s true state (positional information) in the remote environment, often create complications and make it harder for the system to control the robot. In this paper, we propose a new approach for state estimation assuming uncertain delayed sensor measurements of a Telepresence robot during navigation. A new real world experimental model, based on Augmented State Extended Kalman Filter (AS-EKF), is proposed to estimate the true position of the Telepresence robot. The uncertainty of the delayed sensor measurements have been modelled using probabilistic density functions (PDF). The proposed model was successfully verified in our proposed experimental framework which consists of a state-of-the-art differential-drive Telepresence robot and a motion tracking multi-camera system. The results show significant improvements compared to the traditional EKF that does not consider uncertain delays in sensor measurements. The proposed model will be beneficial to build a real time predictive display by reducing the effect of visual delay to navigate the robot under the operator\u2019s control command, without waiting for delayed sensor measurements.

A practical setup for control engineering courses

A. Chevalier, K. Dekemele, J. Juchem and M. Loccufier, Student Feedback on Educational Innovation in Control Engineering: Active Learning in Practice, IEEE Transactions on Education, vol. 64, no. 4, pp. 432-437, Nov. 2021, DOI: 10.1109/TE.2021.3077278.

Contribution: An education innovation in control engineering using practical setups and its evaluation based on a three-year student feedback study and examination grades. Background: Based on extensive research, education\u2019s transition toward active learning and more practical experience has been shown to increase learning outcomes. Contrary to virtual and remote labs, a practical session with an individual setup for each student provides the most practical experience. Intended Outcomes: To show a positive effect on learning performance by integrating practical sessions in basic control engineering. Application Design: Presenting low cost setups that can be mass produced and adapt to the course\u2019s growing complexity. These setups are evaluated during a three-year feedback study. Findings: The developed setups increased understanding of theoretical concepts. The new methodology significantly improved students\u2019 average grades. The students\u2019 interest in control theory is triggered. This case study could guide other institutions toward successfully implementing highly individual practical sessions for large groups.

On the increasing problem of writing quality of engineering students

F. C. Berry, M. L. Phillips, J. Condron and P. A. Sanger, Improving Writing Quality of Capstone Reports, IEEE Transactions on Education, vol. 64, no. 4, pp. 383-389, Nov. 2021, DOI: 10.1109/TE.2021.3059739.

Contributions: The main contribution is to share a series of practical methods that improve the writing quality of capstone reports. Background: The ability to write well is critical to the success of an engineering technology graduate. However, the evidence points to the fact that industries are disappointed with the quality of writing skills graduates demonstrate. Intended Outcomes: A faculty review of capstone reports showed little improvement in writing quality from the first course to the second in a two-semester capstone sequence. Therefore, the instructors explored what actions were needed to improve the writing quality of the capstone reports. Application Design: Several changes in the capstone courses were developed and implemented. The changes included: 1) using instructional technology as a scaffolding to help frame the writing required for the course and 2) engaging students in iterative writing with feedback. Findings: The assessment data showed a significant improvement, at the 5% level. The iterative process of writing and rewriting the report, coupled with frequent meetings with faculty mentors, proved to be a powerful combination for improving the writing process of the students.

Analysis of the under-optimality of path lengths when path planning is carried out on a grid instead of the continuous world

James P. Bailey, Alex Nash, Craig A. Tovey, Sven Koenig, Path-length analysis for grid-based path planning, Artificial Intelligence, Volume 301, 2021, DOI: 10.1016/j.artint.2021.103560.

In video games and robotics, one often discretizes a continuous 2D environment into a regular grid with blocked and unblocked cells and then finds shortest paths for the agents on the resulting grid graph. Shortest grid paths, of course, are not necessarily true shortest paths in the continuous 2D environment. In this article, we therefore study how much longer a shortest grid path can be than a corresponding true shortest path on all regular grids with blocked and unblocked cells that tessellate continuous 2D environments. We study 5 different vertex connectivities that result from both different tessellations and different definitions of the neighbors of a vertex. Our path-length analysis yields either tight or asymptotically tight worst-case bounds in a unified framework. Our results show that the percentage by which a shortest grid path can be longer than a corresponding true shortest path decreases as the vertex connectivity increases. Our path-length analysis is topical because it determines the largest path-length reduction possible for any-angle path-planning algorithms (and thus their benefit), a class of path-planning algorithms in artificial intelligence and robotics that has become popular.

Building explanations for AI plans by modifying user’s models to make those plans optimal within them

Sarath Sreedharan, Tathagata Chakraborti, Subbarao Kambhampati, Foundations of explanations as model reconciliation, Artificial Intelligence, Volume 301,
2021, DOI: 10.1016/j.artint.2021.103558.

Past work on plan explanations primarily involved the AI system explaining the correctness of its plan and the rationale for its decision in terms of its own model. Such soliloquy is wholly inadequate in most realistic scenarios where users have domain and task models that differ from that used by the AI system. We posit that the explanations are best studied in light of these differing models. In particular, we show how explanation can be seen as a \u201cmodel reconciliation problem\u201d (MRP), where the AI system in effect suggests changes to the user’s mental model so as to make its plan be optimal with respect to that changed user model. We will study the properties of such explanations, present algorithms for automatically computing them, discuss relevant extensions to the basic framework, and evaluate the performance of the proposed algorithms both empirically and through controlled user studies.

Using a physical simulator for sampled rollouts in stochastic optimal control

Carius J, Ranftl R, Farshidian F, Hutter M. Constrained stochastic optimal control with learned importance sampling: A path integral approach, The International Journal of Robotics Research. 2022;41(2):189-209, DOI: 10.1177/02783649211047890.

Modern robotic systems are expected to operate robustly in partially unknown environments. This article proposes an algorithm capable of controlling a wide range of high-dimensional robotic systems in such challenging scenarios. Our method is based on the path integral formulation of stochastic optimal control, which we extend with constraint-handling capabilities. Under our control law, the optimal input is inferred from a set of stochastic rollouts of the system dynamics. These rollouts are simulated by a physics engine, placing minimal restrictions on the types of systems and environments that can be modeled. Although sampling-based algorithms are typically not suitable for online control, we demonstrate in this work how importance sampling and constraints can be used to effectively curb the sampling complexity and enable real-time control applications. Furthermore, the path integral framework provides a natural way of incorporating existing control architectures as ancillary controllers for shaping the sampling distribution. Our results reveal that even in cases where the ancillary controller would fail, our stochastic control algorithm provides an additional safety and robustness layer. Moreover, in the absence of an existing ancillary controller, our method can be used to train a parametrized importance sampling policy using data from the stochastic rollouts. The algorithm may thereby bootstrap itself by learning an importance sampling policy offline and then refining it to unseen environments during online control. We validate our results on three robotic systems, including hardware experiments on a quadrupedal robot.

On how physical movements shape the perception of time

Rose De Kock, Keri Anne Gladhill, Minaz Numa Ali, Wilsaan Mychal Joiner, Martin Wiener, How movements shape the perception of time, Trends in Cognitive Sciences, Volume 25, Issue 11, 2021, Pages 950-963 DOI: 10.1016/j.tics.2021.08.002.

In order to keep up with a changing environment, mobile organisms must be capable of deciding both where and when to move. This precision necessitates a strong sense of time, as otherwise we would fail in many of our movement goals. Yet, despite this intrinsic link, only recently have researchers begun to understand how these two features interact. Primarily, two effects have been observed: movements can bias time estimates, but they can also make them more precise. Here we review this literature and propose that both effects can be explained by a Bayesian cue combination framework, in which movement itself affords the most precise representation of time, which can influence perception in either feedforward or active sensing modes.

More efficient pose-graph optimization by using the cycles (loop closures) in the graph as a basis, and a nice summary of conventional pose-graph optimization

F. Bai, T. Vidal-Calleja and G. Grisetti, Sparse Pose Graph Optimization in Cycle Space, .IEEE Transactions on Robotics, vol. 37, no. 5, pp. 1381-1400, Oct 2021 DOI: 10.1109/TRO.2021.3050328.

The state-of-the-art modern pose-graph optimization (PGO) systems are vertex based. In this context, the number of variables might be high, albeit the number of cycles in the graph (loop closures) is relatively low. For sparse problems particularly, the cycle space has a significantly smaller dimension than the number of vertices. By exploiting this observation, in this article, we propose an alternative solution to PGO that directly exploits the cycle space. We characterize the topology of the graph as a cycle matrix, and reparameterize the problem using relative poses, which are further constrained by a cycle basis of the graph. We show that by using a minimum cycle basis, the cycle-based approach has superior convergence properties against its vertex-based counterpart, in terms of convergence speed and convergence to the global minimum. For sparse graphs, our cycle-based approach is also more time efficient than the vertex-based. As an additional contribution of this work, we present an effective algorithm to compute the minimum cycle basis. Albeit known in computer science, we believe that this algorithm is not familiar to the robotics community. All the claims are validated by experiments on both standard benchmarks and simulated datasets. To foster the reproduction of the results, we provide a complete open-source C++ implementation 1 of our approach.