A practical setup for control engineering courses

September 25, 2023 11:24 ,

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.

Posted in: Control Engineering , Tagged: ,

On the increasing problem of writing quality of engineering students

September 25, 2023 11:20 ,

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.

Posted in: Education , Tagged: ,

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

September 15, 2023 10:15 ,

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.

Posted in: Artificial Intelligence, Robot task planning , Tagged:

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

September 15, 2023 10:11 ,

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.

Posted in: Artificial Intelligence , Tagged: ,

Using a physical simulator for sampled rollouts in stochastic optimal control

September 15, 2023 09:47 ,

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.

Posted in: Applications of reinforcement learning to control engineering , Tagged: , ,

On how physical movements shape the perception of time

September 15, 2023 09:28 ,

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.

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

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

September 15, 2023 09:21 ,

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.

Posted in: Mobile robot SLAM , Tagged: ,

Safety in MDPs by measuring the probability of reaching dangerous states

July 21, 2023 09:54 ,

Rafal Wisniewski, Luminita-Manuela Bujorianu, Safety of stochastic systems: An analytic and computational approach, . Automatica, Volume 133, 2021 DOI: 10.1016/j.automatica.2021.109839.

We refine the concept of stochastic reach avoidance for a general class of Markov processes introducing a threshold of p for the reaching probability. This new problem is called p-safety, and it aims to ensure that the given process reaches a forbidden set before leaving its ‘working’ state space with a probability of less than p. In the situation when an initial probability measure characterizes the initial states, a variant of p-safety is put forward. We call this form of safety weak p-safety. In this work, we characterize both p-safety and weak p-safety and show how to compute them. We employ semi-definite programming to compute p-safety and linear programming to compute weak p-safety. To get to this point, we use certificates of positivity of polynomials translated into the sum of squares and the Bernstein forms.

Posted in: Artificial Intelligence , Tagged: ,

Solving the “self-recognition on a mirror” problem for robots

July 21, 2023 09:49 ,

Arianna Pipitone, Antonio Chella, Robot passes the mirror test by inner speech, . Robotics and Autonomous Systems, Volume 144, 2021 DOI: 10.1016/j.robot.2021.103838.

The mirror test is a well-known task in Robotics. The existing strategies are based on kinesthetic-visual matching techniques and manipulate perceptual and motion data. The proposed work attempts to demonstrate that it is possible to implement a robust robotic self-recognition method by the inner speech, i.e. the self-dialogue that enables reasoning on symbolic information. The robot self-talks and conceptually reasons on the symbolic forms of signals, and infers if the robot it sees in the mirror is itself or not. The idea is supported by the existing literature in psychology, where the importance of inner speech in self-reflection and self-concept emergence for solving the mirror test was empirically demonstrated.

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

Learning rewards from diverse human sources

July 21, 2023 09:46 ,

Bıyık E, Losey DP, Palan M, Landolfi NC, Shevchuk G, Sadigh D., Learning reward functions from diverse sources of human feedback: Optimally integrating demonstrations and preferences, . The International Journal of Robotics Research. 2022;41(1):45-67 DOI: 10.1177/02783649211041652.

Reward functions are a common way to specify the objective of a robot. As designing reward functions can be extremely challenging, a more promising approach is to directly learn reward functions from human teachers. Importantly, data from human teachers can be collected either passively or actively in a variety of forms: passive data sources include demonstrations (e.g., kinesthetic guidance), whereas preferences (e.g., comparative rankings) are actively elicited. Prior research has independently applied reward learning to these different data sources. However, there exist many domains where multiple sources are complementary and expressive. Motivated by this general problem, we present a framework to integrate multiple sources of information, which are either passively or actively collected from human users. In particular, we present an algorithm that first utilizes user demonstrations to initialize a belief about the reward function, and then actively probes the user with preference queries to zero-in on their true reward. This algorithm not only enables us combine multiple data sources, but it also informs the robot when it should leverage each type of information. Further, our approach accounts for the human’s ability to provide data: yielding user-friendly preference queries which are also theoretically optimal. Our extensive simulated experiments and user studies on a Fetch mobile manipulator demonstrate the superiority and the usability of our integrated framework..

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