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Main Title: No Free Lunch in Ball Catching: A Comparison of Cartesian and Angular Representations for Control
Author(s): Höfer, Sebastian
Raisch, Jörg
Toussaint, Marc
Brock, Oliver
Type: Generic Research Data
Language Code: en
Abstract: How to run most effectively to catch a projectile, such as a baseball, that is flying in the air for a long period of time? The question about the best solution to the ball catching problem has been subject to intense scientific debate for almost 50 years. It turns out that this scientific debate is not focused on the ball catching problem alone, but revolves around the research question what constitutes the ingredients of intelligent decision making. Over time, two opposing views have emerged: the generalist view regarding intelligence as the ability to solve any task without knowing goal and environment in advance, based on optimal decision making using predictive models; and the specialist view which argues that intelligent decision making does not have to be based on predictive models and not even optimal, advocating simple and efficient rules of thumb (heuristics) as superior to enable accurate decisions. We study two types of approaches to the ball catching problem, one for each view, and investigate their properties using both a theoretical analysis and a broad set of simulation experiments. Our study shows that neither of the two types of approaches can be regarded as superior in solving all relevant variants of the ball catching problem: each approach is optimal under a different realistic environmental condition. Therefore, predictive models neither guarantee nor prevent success a priori, and we further show that the key difference between the generalist and the specialist approach to ball catching is the type of input representation used to control the agent. From this finding, we conclude that the right solution to a decision making or control problem is orthogonal to the generalist and specialist approach, and thus requires a reconciliation of the two views in favor of a representation-centric view.
Issue Date: 2018
Date Available: 18-May-2018
DDC Class: 000 Informatik, Informationswissenschaft, allgemeine Werke
Subject(s): ball catching
gaze heuristic
Chapman's strategy
optical acceleration cancellation
optimal control
reinforcement learning
no free lunch
Sponsor/Funder: DFG, SPP 1527, Autonomes Lernen
Appears in Collections:FG Robotics » Research Data

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e2e_baseline_cov-io_manual.zipExperimental evaluation of COV-IO baseline (previously trained by supervised learning)2.28 MBZIP ArchiveView/Open
e2e_baseline_cov-oac_manual.zipExperimental evaluation of COV-OAC baseline (previously trained by supervised learning)2.4 MBZIP ArchiveView/Open
e2e_cma.zipCMA-ES evaluated to different observation types123.23 MBZIP ArchiveView/Open
2DExperiments_fr60.tar.gzExperiments in 2D environment with frame rate of 60Hz2.32 GBGZip Tar ArchiveView/Open
2DExperiments_fr10.tar.gzExperiments in 2D environment with frame rate of 10Hz (including MPC experiments)182.31 MBGZip Tar ArchiveView/Open
AdversarialConstCOV.tar.gzSingle experiment, demonstrating adversarial parameter choice for constant COV strategy (Figure 5 in paper)1.6 MBGZip Tar ArchiveView/Open
3DExperiments_fr10.tar.gzExperiments in 3D environment with frame rate of 10Hz (including MPC experiments)734.48 MBGZip Tar ArchiveView/Open
3DExperiments_fr60_part1.tar.gzExperiments in 3D environment with frame rate of 60Hz (1: COV-IO)1.84 GBGZip Tar ArchiveView/Open
3DExperiments_fr60_part2.tar.gzExperiments in 3D environment with frame rate of 60Hz (2: COV-OAC)1.84 GBGZip Tar ArchiveView/Open
3DExperiments_fr60_part3.tar.gzExperiments in 3D environment with frame rate of 60Hz (3: iLQG)1.95 GBGZip Tar ArchiveView/Open
README.mdREADME1.14 kBMarkdown (Text)View/Open
3DExperiments_fr60_part4.tar.gzExperiments in 3D environment with frame rate of 60Hz (4: LQG)1.9 GBGZip Tar ArchiveView/Open

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