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2018


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Direct Sparse Odometry With Rolling Shutter

Schubert, D., Usenko, V., Demmel, N., Stueckler, J., Cremers, D.

European Conference on Computer Vision (ECCV), September 2018, accepted as oral presentation (conference)

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[BibTex]

2018


[BibTex]


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Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry

Yang, N., Wang, R., Stueckler, J., Cremers, D.

European Conference on Computer Vision (ECCV), September 2018, accepted as oral presentation, arXiv 1807.02570 (conference)

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link (url) [BibTex]

link (url) [BibTex]


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Discovering and Teaching Optimal Planning Strategies

Lieder, F., Callaway, F., Krueger, P. M., Das, P., Griffiths, T. L., Gul, S.

In The 14th biannual conference of the German Society for Cognitive Science, GK, September 2018, Falk Lieder and Frederick Callaway contributed equally to this publication. (inproceedings)

Abstract
How should we think and decide, and how can we learn to make better decisions? To address these questions we formalize the discovery of cognitive strategies as a metacognitive reinforcement learning problem. This formulation leads to a computational method for deriving optimal cognitive strategies and a feedback mechanism for accelerating the process by which people learn how to make better decisions. As a proof of concept, we apply our approach to develop an intelligent system that teaches people optimal planning stratgies. Our training program combines a novel process-tracing paradigm that makes peoples latent planning strategies observable with an intelligent system that gives people feedback on how their planning strategy could be improved. The pedagogy of our intelligent tutor is based on the theory that people discover their cognitive strategies through metacognitive reinforcement learning. Concretely, the tutor’s feedback is designed to maximally accelerate people’s metacognitive reinforcement learning towards the optimal cognitive strategy. A series of four experiments confirmed that training with the cognitive tutor significantly improved people’s decision-making competency: Experiment 1 demonstrated that the cognitive tutor’s feedback accelerates participants’ metacognitive learning. Experiment 2 found that this training effect transfers to more difficult planning problems in more complex environments. Experiment 3 found that these transfer effects are retained for at least 24 hours after the training. Finally, Experiment 4 found that practicing with the cognitive tutor conveys additional benefits above and beyond verbal description of the optimal planning strategy. The results suggest that promoting metacognitive reinforcement learning with optimal feedback is a promising approach to improving the human mind.

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link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Discovering Rational Heuristics for Risky Choice

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

The 14th biannual conference of the German Society for Cognitive Science, GK, The 14th biannual conference of the German Society for Cognitive Science, GK, September 2018 (conference)

Abstract
How should we think and decide to make the best possible use of our precious time and limited cognitive resources? And how do people’s cognitive strategies compare to this ideal? We study these questions in the domain of multi-alternative risky choice using the methodology of resource-rational analysis. To answer the first question, we leverage a new meta-level reinforcement learning algorithm to derive optimal heuristics for four different risky choice environments. We find that our method rediscovers two fast-and-frugal heuristics that people are known to use, namely Take-The-Best and choosing randomly, as resource-rational strategies for specific environments. Our method also discovered a novel heuristic that combines elements of Take-The-Best and Satisficing. To answer the second question, we use the Mouselab paradigm to measure how people’s decision strategies compare to the predictions of our resource-rational analysis. We found that our resource-rational analysis correctly predicted which strategies people use and under which conditions they use them. While people generally tend to make rational use of their limited resources overall, their strategy choices do not always fully exploit the structure of each decision problem. Overall, people’s decision operations were about 88% as resource-rational as they could possibly be. A formal model comparison confirmed that our resource-rational model explained people’s decision strategies significantly better than the Directed Cognition model of Gabaix et al. (2006). Our study is a proof-of-concept that optimal cognitive strategies can be automatically derived from the principle of resource-rationality. Our results suggest that resource-rational analysis is a promising approach for uncovering people’s cognitive strategies and revisiting the debate about human rationality with a more realistic normative standard.

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link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Learning to Select Computations

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

In Uncertainty in Artificial Intelligence: Proceedings of the Thirty-Fourth Conference, August 2018, Frederick Callaway and Sayan Gul and Falk Lieder contributed equally to this publication. (inproceedings)

Abstract
The efficient use of limited computational resources is an essential ingredient of intelligence. Selecting computations optimally according to rational metareasoning would achieve this, but this is computationally intractable. Inspired by psychology and neuroscience, we propose the first concrete and domain-general learning algorithm for approximating the optimal selection of computations: Bayesian metalevel policy search (BMPS). We derive this general, sample-efficient search algorithm for a computation-selecting metalevel policy based on the insight that the value of information lies between the myopic value of information and the value of perfect information. We evaluate BMPS on three increasingly difficult metareasoning problems: when to terminate computation, how to allocate computation between competing options, and planning. Across all three domains, BMPS achieved near-optimal performance and compared favorably to previously proposed metareasoning heuristics. Finally, we demonstrate the practical utility of BMPS in an emergency management scenario, even accounting for the overhead of metareasoning.

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link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


A resource-rational analysis of human planning
A resource-rational analysis of human planning

Callaway, F., Lieder, F., Das, P., Gul, S., Krueger, P. M., Griffiths, T. L.

In Proceedings of the 40th Annual Conference of the Cognitive Science Society, May 2018, Frederick Callaway and Falk Lieder contributed equally to this publication. (inproceedings)

Abstract
People's cognitive strategies are jointly shaped by function and computational constraints. Resource-rational analysis leverages these constraints to derive rational models of people's cognitive strategies from the assumption that people make rational use of limited cognitive resources. We present a resource-rational analysis of planning and evaluate its predictions in a newly developed process tracing paradigm. In Experiment 1, we find that a resource-rational planning strategy predicts the process by which people plan more accurately than previous models of planning. Furthermore, in Experiment 2, we find that it also captures how people's planning strategies adapt to the structure of the environment. In addition, our approach allows us to quantify for the first time how close people's planning strategies are to being resource-rational and to characterize in which ways they conform to and deviate from optimal planning.

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DOI [BibTex]

DOI [BibTex]


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The TUM VI Benchmark for Evaluating Visual-Inertial Odometry

Schubert, D., Goll, T., Demmel, N., Usenko, V., Stueckler, J., Cremers, D.

In IEEE International Conference on Intelligent Robots and Systems (IROS), 2018, arXiv:1804.06120 (inproceedings)

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[BibTex]

[BibTex]


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Variational Network Quantization

Achterhold, J., Koehler, J. M., Schmeink, A., Genewein, T.

In International Conference on Learning Representations , 2018 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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Light field intrinsics with a deep encoder-decoder network

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

In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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Sublabel-accurate convex relaxation with total generalized variation regularization

(DAGM Best Master's Thesis Award)

Strecke, M., Goldluecke, B.

In German Conference on Pattern Recognition (Proc. GCPR), 2018 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]

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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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]

2013


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Controllability and Resource-Rational Planning

Lieder, F., Goodman, N. D., Huys, Q. J.

In Computational and Systems Neuroscience (Cosyne), pages: 112, 2013 (inproceedings)

Abstract
Learned helplessness experiments involving controllable vs. uncontrollable stressors have shown that the perceived ability to control events has profound consequences for decision making. Normative models of decision making, however, do not naturally incorporate knowledge about controllability, and previous approaches to incorporating it have led to solutions with biologically implausible computational demands [1,2]. Intuitively, controllability bounds the differential rewards for choosing one strategy over another, and therefore believing that the environment is uncontrollable should reduce one’s willingness to invest time and effort into choosing between options. Here, we offer a normative, resource-rational account of the role of controllability in trading mental effort for expected gain. In this view, the brain not only faces the task of solving Markov decision problems (MDPs), but it also has to optimally allocate its finite computational resources to solve them efficiently. This joint problem can itself be cast as a MDP [3], and its optimal solution respects computational constraints by design. We start with an analytic characterisation of the influence of controllability on the use of computational resources. We then replicate previous results on the effects of controllability on the differential value of exploration vs. exploitation, showing that these are also seen in a cognitively plausible regime of computational complexity. Third, we find that controllability makes computation valuable, so that it is worth investing more mental effort the higher the subjective controllability. Fourth, we show that in this model the perceived lack of control (helplessness) replicates empirical findings [4] whereby patients with major depressive disorder are less likely to repeat a choice that led to a reward, or to avoid a choice that led to a loss. Finally, the model makes empirically testable predictions about the relationship between reaction time and helplessness.

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[BibTex]

2013


[BibTex]


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Efficient Dense 3D Rigid-Body Motion Segmentation in RGB-D Video

Stueckler, J., Behnke, S.

In Proc. of the British Machine Vision Conference (BMVC), 2013 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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Mobile bin picking with an anthropomorphic service robot

Nieuwenhuisen, M., Droeschel, D., Holz, D., Stueckler, J., Berner, A., Li, J., Klein, R., Behnke, S.

In Proc. of the IEEE International Conference on Robotics and Automation (ICRA), pages: 2327-2334, May 2013 (inproceedings)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Learned helplessness and generalization

Lieder, F., Goodman, N. D., Huys, Q. J. M.

In 35th Annual Conference of the Cognitive Science Society, 2013 (inproceedings)

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[BibTex]

[BibTex]


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Multi-resolution surfel mapping and real-time pose tracking using a continuously rotating 2D laser scanner

Schadler, M., Stueckler, J., Behnke, S.

In Proc. of the IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), pages: 1-6, October 2013 (inproceedings)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Joint detection and pose tracking of multi-resolution surfel models in RGB-D

McElhone, M., Stueckler, J., Behnke, S.

In Proc. of the European Conference on Mobile Robots (ECMR), pages: 131-137, IEEE, 2013 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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Reverse-Engineering Resource-Efficient Algorithms

Lieder, F., Goodman, N. D., Griffiths, T. L.

In NIPS Workshop Resource-Efficient Machine Learning, 2013 (inproceedings)

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[BibTex]

[BibTex]


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Distinctive 3D surface entropy features for place recognition.

Fiolka, T., Stueckler, J., Klein, D. A., Schulz, D., Behnke, S.

In Proc. of the European Conference on Mobile Robots (ECMR), pages: 204-209, IEEE, 2013 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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Combining contour and shape primitives for object detection and pose estimation of prefabricated parts

Berner, A., Li, J., Holz, D., Stueckler, J., Behnke, S., Klein, R.

In Proc. of the 20th IEEE International Conference on Image Processing (ICIP), pages: 3326-3330, sep 2013 (inproceedings)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Hierarchical Object Discovery and Dense Modelling From Motion Cues in RGB-D Video

Stueckler, J., Behnke, S.

In Proc. of the 23rd International Joint Conference on Artificial Intelligence (IJCAI), IJCAI/AAAI, 2013 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]

2010


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Combining depth and color cues for scale- and viewpoint-invariant object segmentation and recognition using Random Forests

Stueckler, J., Behnke, S.

In Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), pages: 4566-4571, October 2010 (inproceedings)

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link (url) DOI [BibTex]

2010


link (url) DOI [BibTex]


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Intuitive Multimodal Interaction for Domestic Service Robots

Nieuwenhuisen, M., Stueckler, J., Behnke, S.

In Proc. of the ISR/ROBOTIK, VDE Verlag, 2010 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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Improving People Awareness of Service Robots by Semantic Scene Knowledge

Stueckler, J., Behnke, S.

In RobuCup, 6556, pages: 157-168, Lecture Notes in Computer Science, Springer, 2010 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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Towards Semantic Scene Analysis with Time-of-flight Cameras

Holz, D., Schnabel, R., Droeschel, D., Stueckler, J., Behnke, S.

In RobuCup, 6556, pages: 121-132, Lecture Notes in Computer Science, Springer, 2010 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]


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Utilizing the Structure of Field Lines for Efficient Soccer Robot Localization

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

In RobuCup, 6556, pages: 397-408, Lecture Notes in Computer Science, Springer, 2010 (inproceedings)

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link (url) [BibTex]

link (url) [BibTex]