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2017


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Optimal gamification can help people procrastinate less

Lieder, F., Griffiths, T. L.

Annual Meeting of the Society for Judgment and Decision Making, Annual Meeting of the Society for Judgment and Decision Making, November 2017 (conference)

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Project Page [BibTex]

2017


Project Page [BibTex]


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From Monocular SLAM to Autonomous Drone Exploration

von Stumberg, L., Usenko, V., Engel, J., Stueckler, J., Cremers, D.

In European Conference on Mobile Robots (ECMR), September 2017 (inproceedings)

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[BibTex]

[BibTex]


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Multi-View Deep Learning for Consistent Semantic Mapping with RGB-D Cameras

Ma, L., Stueckler, J., Kerl, C., Cremers, D.

In IEEE International Conference on Intelligent Robots and Systems (IROS), Vancouver, Canada, 2017 (inproceedings)

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[BibTex]

[BibTex]


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Accurate depth and normal maps from occlusion-aware focal stack symmetry

Strecke, M., Alperovich, A., Goldluecke, B.

In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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An automatic method for discovering rational heuristics for risky choice

Lieder, F., Krueger, P. M., Griffiths, T. L.

In Proceedings of the 39th Annual Meeting of the Cognitive Science Society. Austin, TX: Cognitive Science Society, 2017, Falk Lieder and Paul M. Krueger contributed equally to this publication. (inproceedings)

Abstract
What is the optimal way to make a decision given that your time is limited and your cognitive resources are bounded? To answer this question, we formalized the bounded optimal decision process as the solution to a meta-level Markov decision process whose actions are costly computations. We approximated the optimal solution and evaluated its predictions against human choice behavior in the Mouselab paradigm, which is widely used to study decision strategies. Our computational method rediscovered well-known heuristic strategies and the conditions under which they are used, as well as novel heuristics. A Mouselab experiment confirmed our model’s main predictions. These findings are a proof-of-concept that optimal cognitive strategies can be automatically derived as the rational use of finite time and bounded cognitive resources.

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Project Page [BibTex]

Project Page [BibTex]


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Semi-Supervised Deep Learning for Monocular Depth Map Prediction

Kuznietsov, Y., Stueckler, J., Leibe, B.

In IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2017 (inproceedings)

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[BibTex]

[BibTex]


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Shadow and Specularity Priors for Intrinsic Light Field Decomposition

Alperovich, A., Johannsen, O., Strecke, M., Goldluecke, B.

In Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), 2017 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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Keyframe-Based Visual-Inertial Online SLAM with Relocalization

Kasyanov, A., Engelmann, F., Stueckler, J., Leibe, B.

In IEEE/RSJ Int. Conference on Intelligent Robots and Systems, IROS, 2017 (inproceedings)

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[BibTex]

[BibTex]


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A reward shaping method for promoting metacognitive learning

Lieder, F., Krueger, P. M., Callaway, F., Griffiths, T. L.

In Proceedings of the Third Multidisciplinary Conference on Reinforcement Learning and Decision-Making, 2017 (inproceedings)

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Project Page [BibTex]

Project Page [BibTex]


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SAMP: Shape and Motion Priors for 4D Vehicle Reconstruction

Engelmann, F., Stueckler, J., Leibe, B.

In IEEE Winter Conference on Applications of Computer Vision, WACV, 2017 (inproceedings)

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[BibTex]

[BibTex]


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When does bounded-optimal metareasoning favor few cognitive systems?

Milli, S., Lieder, F., Griffiths, T. L.

In AAAI Conference on Artificial Intelligence, 31, 2017 (inproceedings)

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[BibTex]

[BibTex]


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The Structure of Goal Systems Predicts Human Performance

Bourgin, D., Lieder, F., Reichman, D., Talmon, N., Griffiths, T.

In Proceedings of the 39th Annual Meeting of the Cognitive Science Society, 2017 (inproceedings)

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[BibTex]

[BibTex]


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Learning to (mis) allocate control: maltransfer can lead to self-control failure

Bustamante, L., Lieder, F., Musslick, S., Shenhav, A., Cohen, J.

In The 3rd Multidisciplinary Conference on Reinforcement Learning and Decision Making. Ann Arbor, Michigan, 2017 (inproceedings)

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[BibTex]

[BibTex]


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Mouselab-MDP: A new paradigm for tracing how people plan

Callaway, F., Lieder, F., Krueger, P. M., Griffiths, T. L.

In The 3rd multidisciplinary conference on reinforcement learning and decision making, 2017 (inproceedings)

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[BibTex]

[BibTex]


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Enhancing metacognitive reinforcement learning using reward structures and feedback

Krueger, P. M., Lieder, F., Griffiths, T. L.

In Proceedings of the 39th Annual Meeting of the Cognitive Science Society, 2017 (inproceedings)

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Project Page Project Page [BibTex]

Project Page Project Page [BibTex]


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Helping people choose subgoals with sparse pseudo rewards

Callaway, F., Lieder, F., Griffiths, T. L.

In Proceedings of the Third Multidisciplinary Conference on Reinforcement Learning and Decision Making, 2017 (inproceedings)

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[BibTex]

[BibTex]

2014


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Adaptive Tool-Use Strategies for Anthropomorphic Service Robots

Stueckler, J., Behnke, S.

In Proc. of the 14th IEEE-RAS International Conference on Humanoid Robots (Humanoids), 2014 (inproceedings)

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link (url) [BibTex]

2014


link (url) [BibTex]


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Algorithm selection by rational metareasoning as a model of human strategy selection

Lieder, F., Plunkett, D., Hamrick, J. B., Russell, S. J., Hay, N. J., Griffiths, T. L.

In Advances in Neural Information Processing Systems 27, 2014 (inproceedings)

Abstract
Selecting the right algorithm is an important problem in computer science, because the algorithm often has to exploit the structure of the input to be efficient. The human mind faces the same challenge. Therefore, solutions to the algorithm selection problem can inspire models of human strategy selection and vice versa. Here, we view the algorithm selection problem as a special case of metareasoning and derive a solution that outperforms existing methods in sorting algorithm selection. We apply our theory to model how people choose between cognitive strategies and test its prediction in a behavioral experiment. We find that people quickly learn to adaptively choose between cognitive strategies. People's choices in our experiment are consistent with our model but inconsistent with previous theories of human strategy selection. Rational metareasoning appears to be a promising framework for reverse-engineering how people choose among cognitive strategies and translating the results into better solutions to the algorithm selection problem.

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Project Page [BibTex]

Project Page [BibTex]


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Local Multi-Resolution Surfel Grids for MAV Motion Estimation and 3D Mapping

Droeschel, D., Stueckler, J., Behnke, S.

In Proc. of the 13th International Conference on Intelligent Autonomous Systems (IAS), 2014 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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Combining the Strengths of Sparse Interest Point and Dense Image Registration for RGB-D Odometry

Stueckler, J., Gutt, A., Behnke, S.

In Proc. of the Joint 45th International Symposium on Robotics (ISR) and 8th German Conference on Robotics (ROBOTIK), 2014 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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Mobile Teleoperation Interfaces with Adjustable Autonomy for Personal Service Robots

Schwarz, M., Stueckler, J., Behnke, S.

In Proceedings of the 2014 ACM/IEEE International Conference on Human-robot Interaction, pages: 288-289, HRI ’14, ACM, 2014 (inproceedings)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Self-Exploration of the Stumpy Robot with Predictive Information Maximization

Martius, G., Jahn, L., Hauser, H., V. Hafner, V.

In Proc. From Animals to Animats, SAB 2014, 8575, pages: 32-42, LNCS, Springer, 2014 (inproceedings)

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[BibTex]

[BibTex]


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Efficient deformable registration of multi-resolution surfel maps for object manipulation skill transfer

Stueckler, J., Behnke, S.

In Proc. of the IEEE International Conference on Robotics and Automation (ICRA), pages: 994-1001, May 2014 (inproceedings)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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The high availability of extreme events serves resource-rational decision-making

Lieder, F., Hsu, M., Griffiths, T. L.

In Proceedings of the 36th Annual Conference of the Cognitive Science Society, 2014 (inproceedings)

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[BibTex]

[BibTex]


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Local multi-resolution representation for 6D motion estimation and mapping with a continuously rotating 3D laser scanner

Droeschel, D., Stueckler, J., Behnke, S.

In Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA), pages: 5221-5226, May 2014 (inproceedings)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Layers of Abstraction: (Neuro)computational models of learning local and global statistical regularities

Diaconescu, A., Lieder, F., Mathys, C., Stephan, K. E.

In 20th Annual Meeting of the Organization for Human Brain Mapping, 2014 (inproceedings)

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[BibTex]

[BibTex]

2008


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Emergence of Interaction Among Adaptive Agents

Martius, G., Nolfi, S., Herrmann, J. M.

In Proc. From Animals to Animats 10 (SAB 2008), 5040, pages: 457-466, LNCS, Springer, 2008 (inproceedings)

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DOI [BibTex]

2008


DOI [BibTex]


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In-lane Localization in Road Networks using Curbs Detected in Omnidirectional Height Images

Stueckler, J., Schulz, H., Behnke, S.

In Proceedings of Robotik 2008, 2008 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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Structure from Behavior in Autonomous Agents

Martius, G., Fiedler, K., Herrmann, J.

In Proc. IEEE Intl. Conf. Intelligent Robots and Systems (IROS 2008), pages: 858 - 862, 2008 (inproceedings)

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DOI [BibTex]

DOI [BibTex]


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Orthogonal wall correction for visual motion estimation

Stueckler, J., Behnke, S.

In Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA), pages: 1-6, May 2008 (inproceedings)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]