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- Eriksson Sörman, Daniel, 1974-, et al.
(författare)
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Relationships between Dota 2 expertise and decision-making ability
- 2022
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Ingår i: PLOS ONE. - : Public Library of Science. - 1932-6203. ; 17:3
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Tidskriftsartikel (refereegranskat)abstract
- Esports is an often time-consuming activity that has become increasingly popular with billions of players all over the world. The objective of this study was to investigate if there is a relationship between skill level in the strategy video game Dota 2, a game that places many demands on decision making to be successful, and decision making under ambiguity and experience as measured by performance in the Iowa Gambling Task (IGT), a task known to have ecological validity. Two indicators of players’ performance in Dota 2, namely match-making rating (MMR) and Medal, were used as predictors of performance in the IGT in path models. Results showed that Medal was a significant predictor of performance in IGT, while MMR score was borderline significant. The cognitive reflection task, included in the models as an indicator of the ability to engage in conscious, analytical, rational, and logical thinking, was a significant predictor of performance in IGT, and was significantly and positively related to MMR. The findings from this study give insight into the cognitive demands related to performance in Dota 2. Although results suggest that strategy video gaming may be a factor that contributes to increased decision making abilities, a reversed relationship is also possible, whereby individuals who are better at decision making are also more likely to become successful in Dota-2. More studies, preferably longitudinal, are needed to replicate the findings of this study and to establish the directionality between factors.
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- Andersson, Jennifer, et al.
(författare)
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Reinforcement Learning Control of a Forestry Crane Manipulator
- 2021
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Ingår i: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021). - Prague : IEEE Robotics and Automation Society. - 9781665417150 - 9781665417143 ; , s. 2121-2126
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Konferensbidrag (refereegranskat)abstract
- Forestry machines are heavy vehicles performing complex manipulation tasks in unstructured production forest environments. Together with the complex dynamics of the on-board hydraulically actuated cranes, the rough forest terrains have posed a particular challenge in forestry automation. In this study, the feasibility of applying reinforcement learning control to forestry crane manipulators is investigated in a simulated environment. Our results show that it is possible to learn successful actuator-space control policies for energy efficient log grasping by invoking a simple curriculum in a deep reinforcement learning setup. Given the pose of the selected logs, our best control policy reaches a grasping success rate of 97%. Including an energy-optimization goal in the reward function, the energy consumption is significantly reduced compared to control policies learned without incentive for energy optimization, while the increase in cycle time is marginal. The energy-optimization effects can be observed in the overall smoother motion and acceleration profiles during crane manipulation.
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- Aoshima, Koji, et al.
(författare)
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Examining the simulation-to-reality-gap of a wheel loader interacting with deformable terrain
- 2022
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Simulators are essential for developing autonomous control of off-road vehicles and heavy equipment. They allow automatic testing under safe and controllable conditions, and the generation of large amounts of synthetic and annotated training data necessary for deep learning to be applied [1]. Limiting factors are the computational speed and how accurately the simulator reflects the real system. When the deviation is too large, a controller transfers poorly from the simulated to the real environment. On the other hand, a finely resolved simulator easily becomes too computationally intense and slow for running the necessary number of simulations or keeping realtime pace with hardware in the loop.We investigate how well a physics-based simulator can be made to match its physical counterpart, a full-scale wheel loader instrumented with motion and force sensors performing a bucket filling operation [2]. The simulated vehicle is represented as a rigid multibody system with nonsmooth contact and driveline dynamics. The terrain model combines descriptions of the frictional-cohesive soil as a continuous solid and particles, discretized in voxels and discrete elements [3]. Strong and stable force coupling with the equipment is mediated via rigid aggregate bodies capturing the bulk mechanics of the soil. The results include analysis of the agreement between a calibrated simulation model and the field tests, and of how the simulation performance and accuracy depend on spatial and temporal resolution. The system’s degrees of freedom range from hundreds to millions and the simulation speed up to ten times faster than realtime. Furthermore, it is investigated how sensitive a deep learning controller is to variations in the simulator environment parameters.[1] S. Backman, D. Lindmark, K. Bodin, M. Servin, J. Mörk, and H. Löfgren. Continuous control of an underground loader using deep reinforcement learning. Machines 9(10): 216 (2021).[2] K. Aoshima, M. Servin, E. Wadbro. Simulation-Based Optimization of High-Performance Wheel Loading. Proc. 38th Int. Symp. Automation and Robotics in Construction (ISARC), Dubai, UAE (2021).[3] M. Servin., T. Berglund., and S. Nystedt. A multiscale model of terrain dynamics for real-time earthmoving simulation. Advanced Modeling and Simulation in Engineering Sciences 8, 11 (2021).
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