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2020


A Gamified App that Helps People Overcome Self-Limiting Beliefs by Promoting Metacognition
A Gamified App that Helps People Overcome Self-Limiting Beliefs by Promoting Metacognition

Amo, V., Lieder, F.

SIG 8 Meets SIG 16, September 2020 (conference) Accepted

Abstract
Previous research has shown that approaching learning with a growth mindset is key for maintaining motivation and overcoming setbacks. Mindsets are systems of beliefs that people hold to be true. They influence a person's attitudes, thoughts, and emotions when they learn something new or encounter challenges. In clinical psychology, metareasoning (reflecting on one's mental processes) and meta-awareness (recognizing thoughts as mental events instead of equating them to reality) have proven effective for overcoming maladaptive thinking styles. Hence, they are potentially an effective method for overcoming self-limiting beliefs in other domains as well. However, the potential of integrating assisted metacognition into mindset interventions has not been explored yet. Here, we propose that guiding and training people on how to leverage metareasoning and meta-awareness for overcoming self-limiting beliefs can significantly enhance the effectiveness of mindset interventions. To test this hypothesis, we develop a gamified mobile application that guides and trains people to use metacognitive strategies based on Cognitive Restructuring (CR) and Acceptance Commitment Therapy (ACT) techniques. The application helps users to identify and overcome self-limiting beliefs by working with aversive emotions when they are triggered by fixed mindsets in real-life situations. Our app aims to help people sustain their motivation to learn when they face inner obstacles (e.g. anxiety, frustration, and demotivation). We expect the application to be an effective tool for helping people better understand and develop the metacognitive skills of emotion regulation and self-regulation that are needed to overcome self-limiting beliefs and develop growth mindsets.

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A gamified app that helps people overcome self-limiting beliefs by promoting metacognition [BibTex]


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Model-Agnostic Counterfactual Explanations for Consequential Decisions

Karimi, A., Barthe, G., Balle, B., Valera, I.

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 108, pages: 895-905, Proceedings of Machine Learning Research, (Editors: Silvia Chiappa and Roberto Calandra), PMLR, August 2020 (conference)

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arXiv link (url) [BibTex]

arXiv link (url) [BibTex]


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Fair Decisions Despite Imperfect Predictions

Kilbertus, N., Gomez Rodriguez, M., Schölkopf, B., Muandet, K., Valera, I.

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 108, pages: 277-287, Proceedings of Machine Learning Research, (Editors: Silvia Chiappa and Roberto Calandra), PMLR, August 2020 (conference)

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link (url) [BibTex]

link (url) [BibTex]


Optimal To-Do List Gamification
Optimal To-Do List Gamification

Stojcheski, J., Felso, V., Lieder, F.

arXiv, August 2020 (techreport)

Abstract
What should I work on first? What can wait until later? Which projects should I prioritize and which tasks are not worth my time? These are challenging questions that many people face every day. People’s intuitive strategy is to prioritize their immediate experience over the long-term consequences. This leads to procrastination and the neglect of important long-term projects in favor of seemingly urgent tasks that are less important. Optimal gamification strives to help people overcome these problems by incentivizing each task by a number of points that communicates how valuable it is in the long-run. Unfortunately, computing the optimal number of points with standard dynamic programming methods quickly becomes intractable as the number of a person’s projects and the number of tasks required by each project increase. Here, we introduce and evaluate a scalable method for identifying which tasks are most important in the long run and incentivizing each task according to its long-term value. Our method makes it possible to create to-do list gamification apps that can handle the size and complexity of people’s to-do lists in the real world.

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link (url) Project Page [BibTex]


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How to navigate everyday distractions: Leveraging optimal feedback to train attention control

Wirzberger, M., Lado, A., Eckerstorfer, L., Oreshnikov, I., Passy, J., Stock, A., Shenhav, A., Lieder, F.

Annual Meeting of the Cognitive Science Society, July 2020 (conference) Accepted

Abstract
To stay focused on their chosen tasks, people have to inhibit distractions. The underlying attention control skills can improve through reinforcement learning, which can be accelerated by giving feedback. We applied the theory of metacognitive reinforcement learning to develop a training app that gives people optimal feedback on their attention control while they are working or studying. In an eight-day field experiment with 99 participants, we investigated the effect of this training on people's productivity, sustained attention, and self-control. Compared to a control condition without feedback, we found that participants receiving optimal feedback learned to focus increasingly better (f = .08, p < .01) and achieved higher productivity scores (f = .19, p < .01) during the training. In addition, they evaluated their productivity more accurately (r = .12, p < .01). However, due to asymmetric attrition problems, these findings need to be taken with a grain of salt.

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How to navigate everyday distractions: Leveraging optimal feedback to train attention control DOI Project Page [BibTex]


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Algorithmic Recourse: from Counterfactual Explanations to Interventions

Karimi, A., Schölkopf, B., Valera, I.

37th International Conference on Machine Learning (ICML), July 2020 (conference) Submitted

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[BibTex]

[BibTex]


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Leveraging Machine Learning to Automatically Derive Robust Planning Strategies from Biased Models of the Environment

Kemtur, A., Jain, Y. R., Mehta, A., Callaway, F., Consul, S., Stojcheski, J., Lieder, F.

CogSci 2020, July 2020, Anirudha Kemtur and Yash Raj Jain contributed equally to this publication. (conference)

Abstract
Teaching clever heuristics is a promising approach to improve decision-making. We can leverage machine learning to discover clever strategies automatically. Current methods require an accurate model of the decision problems people face in real life. But most models are misspecified because of limited information and cognitive biases. To address this problem we develop strategy discovery methods that are robust to model misspecification. Robustness is achieved by model-ing model-misspecification and handling uncertainty about the real-world according to Bayesian inference. We translate our methods into an intelligent tutor that automatically discovers and teaches robust planning strategies. Our robust cognitive tutor significantly improved human decision-making when the model was so biased that conventional cognitive tutors were no longer effective. These findings highlight that our robust strategy discovery methods are a significant step towards leveraging artificial intelligence to improve human decision-making in the real world.

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Project Page [BibTex]

Project Page [BibTex]


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Where Does It End? - Reasoning About Hidden Surfaces by Object Intersection Constraints

Strecke, M., Stückler, J.

In Proceedings IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR), IEEE/CVF International Conference on Computer Vision and Pattern Recognition (CVPR) 2020, June 2020 (inproceedings)

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preprint project page [BibTex]

preprint project page [BibTex]


Changes in Normal Force During Passive Dynamic Touch: Contact Mechanics and Perception
Changes in Normal Force During Passive Dynamic Touch: Contact Mechanics and Perception

Gueorguiev, D., Lambert, J., Thonnard, J., Kuchenbecker, K. J.

In Proceedings of the IEEE Haptics Symposium (HAPTICS), pages: 746-752, Washington, USA, March 2020 (inproceedings)

Abstract
Using a force-controlled robotic platform, we investigated the contact mechanics and psychophysical responses induced by negative and positive modulations in normal force during passive dynamic touch. In the natural state of the finger, the applied normal force modulation induces a correlated change in the tangential force. In a second condition, we applied talcum powder to the fingerpad, which induced a significant modification in the slope of the correlated tangential change. In both conditions, the same ten participants had to detect the interval that contained a decrease or an increase in the pre-stimulation normal force of 1 N. In the natural state, the 75% just noticeable difference for this task was found to be a ratio of 0.19 and 0.18 for decreases and increases, respectively. With talcum powder on the fingerpad, the normal force thresholds remained stable, following the Weber law of constant just noticeable differences, while the tangential force thresholds changed in the same way as the correlation slopes. This result suggests that participants predominantly relied on the normal force changes to perform the detection task. In addition, participants were asked to report whether the force decreased or increased. Their performance was generally poor at this second task even for above-threshold changes. However, their accuracy slightly improved with the talcum powder, which might be due to the reduced finger-surface friction.

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DOI [BibTex]

DOI [BibTex]


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ACTrain: Ein KI-basiertes Aufmerksamkeitstraining für die Wissensarbeit [ACTrain: An AI-based attention training for knowledge work]

Wirzberger, M., Oreshnikov, I., Passy, J., Lado, A., Shenhav, A., Lieder, F.

66th Spring Conference of the German Ergonomics Society, 2020 (conference)

Abstract
Unser digitales Zeitalter lebt von Informationen und stellt unsere begrenzte Verarbeitungskapazität damit täglich auf die Probe. Gerade in der Wissensarbeit haben ständige Ablenkungen erhebliche Leistungseinbußen zur Folge. Unsere intelligente Anwendung ACTrain setzt genau an dieser Stelle an und verwandelt Computertätigkeiten in eine Trainingshalle für den Geist. Feedback auf Basis maschineller Lernverfahren zeigt anschaulich den Wert auf, sich nicht von einer selbst gewählten Aufgabe ablenken zu lassen. Diese metakognitive Einsicht soll zum Durchhalten motivieren und das zugrunde liegende Fertigkeitsniveau der Aufmerksamkeitskontrolle stärken. In laufenden Feldexperimenten untersuchen wir die Frage, ob das Training mit diesem optimalen Feedback die Aufmerksamkeits- und Selbstkontrollfertigkeiten im Vergleich zu einer Kontrollgruppe ohne Feedback verbessern kann.

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link (url) Project Page [BibTex]


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Learning to Identify Physical Parameters from Video Using Differentiable Physics

Kandukuri, R., Achterhold, J., Moeller, M., Stueckler, J.

Accepted for publication at the 42th German Conference on Pattern Recognition (GCPR), 2020 (conference) Accepted

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link (url) [BibTex]

link (url) [BibTex]


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Planning from Images with Deep Latent Gaussian Process Dynamics

Bosch, N., Achterhold, J., Leal-Taixe, L., Stückler, J.

Proceedings of the 2nd Conference on Learning for Dynamics and Control (L4DC), 120, pages: 640-650, Proceedings of Machine Learning Research (PMLR), (Editors: Alexandre M. Bayen and Ali Jadbabaie and George Pappas and Pablo A. Parrilo and Benjamin Recht and Claire Tomlin and Melanie Zeilinger), 2020, arXiv:2005.03770 (conference)

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preprint project page poster [BibTex]

preprint project page poster [BibTex]


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DirectShape: Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation

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

In Proceedings of the IEEE international Conference on Robotics and Automation (ICRA), 2020, arXiv:1904.10097 (inproceedings)

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[BibTex]

[BibTex]


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Learning to Adapt Multi-View Stereo by Self-Supervision

Mallick, A., Stückler, J., Lensch, H.

Proceedings of the British Machine Vision Conference (BMVC), 2020, to appear (conference) To be published

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link (url) [BibTex]

link (url) [BibTex]

2013


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Governance of Humanoid Robot Using Master Exoskeleton

Kumra, S., Mohan, M., Gupta, S., Vaswani, H.

In Proceedings of the IEEE International Symposium on Robotics (ISR), Seoul, South Korea, October 2013 (inproceedings)

Abstract
Dexto:Eka: is an adult-size humanoid robot being developed with the aim of achieving tele-presence. The paper sheds light on the control of this robot using a Master Exoskeleton which comprises of an Exo-Frame, a Control Column and a Graphical User Interface. It further illuminates the processes and algorithms that have been utilized to make an efficient system that would effectively emulate a tele-operator.

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DOI [BibTex]

2013


DOI [BibTex]


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Design and development part 2 of Dexto:Eka: - The humanoid robot

Kumra, S., Mohan, M., Gupta, S., Vaswani, H.

In Proceedings of the International Conference on Mechatronics and Automation (ICMA), Takamatsu, Japan, August 2013 (inproceedings)

Abstract
Through this paper, we elucidate the second phase of the design and development of the tele-operated humanoid robot Dexto:Eka:. Phase one comprised of the development of a 6 DoF left anthropomorphic arm and left exo-frame. Here, we illustrate the development of the right arm, right exo-frame, torso, backbone, human machine interface and omni-directional locomotion system. Dexto:Eka: will be able to communicate with a remote user through Wi-Fi. An exo-frame capacitates it to emulate human arms and its locomotion is controlled by joystick. A Graphical User Interface monitors and helps in controlling the system.

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DOI [BibTex]

DOI [BibTex]