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Matthias Scheutz

25 accepted papers

2026

IntelliProof: An Argumentation Network-based Conversational Helper for Organized Reflection

Kaveh Eskandari Miandoab, Katharine Kowalyshyn, Kabir Pamnani, Anesu Gavhera, Vasanth Sarathy, Matthias Scheutz
AAAI 2026technical

We present IntelliProof, an interactive system for analyzing argumentative essays through LLMs. IntelliProof structures an essay as an argumentation graph, where claims are represented as nodes, supporting evidence is attached as node properties, and edges encode supporting or attacking relations. U

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2026

The Price Is Not Right: Neuro-Symbolic Methods Outperform VLAs on Structured Long-Horizon Manipulation Tasks with Significantly Lower Energy Consumption

Timothy R. Duggan, Pierrick Lorang, Hong Lu, Matthias Scheutz
ICRA 2026poster

Vision-Language-Action (VLA) models have recently been proposed as a pathway toward generalist robotic policies capable of interpreting natural language and visual inputs to generate manipulation actions. However, their effectiveness and efficiency on structured, long-horizon manipulation tasks rema…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2026

Where Norms and References Collide: Evaluating LLMs on Normative Reasoning

Mitchell Abrams, Kaveh Eskandari Miandoab, Felix Gervits, Vasanth Sarathy, Matthias Scheutz
AAAI 2026technical

Embodied agents, such as robots, will need to interact in situated environments where successful communication often depends on reasoning over social norms: shared expectations that constrain what actions are appropriate in context. A key capability in such settings is norm-based reference resolutio

Cited by 0Source ↗PDF ↗Scholar ↗
2025

Curiosity-Driven Imagination: Discovering Plan Operators and Learning Associated Policies for Open-World Adaptation

Pierrick Lorang, Hong Lu, Matthias Scheutz
ICRA 2025

Adapting quickly to dynamic, uncertain environments—often called “open worlds” —remains a major challenge in robotics. Traditional Task and Motion Planning (TAMP) approaches struggle to cope with unforeseen changes, are data-inefficient when adapting, and do not leverage world models during learning

Cited by 3Source ↗Scholar ↗
2025

FLEX: A Framework for Learning Robot-Agnostic Force-Based Skills Involving Sustained Contact Object Manipulation

Shijie Fang, Wenchang Gao, Shivam Goel, Christopher Thierauf, Matthias Scheutz, Jivko Sinapov
ICRA 2025

Learning to manipulate objects efficiently, particularly those involving sustained contact (e.g., pushing, sliding) and articulated parts (e.g., drawers, doors), presents significant challenges. Traditional methods, such as robot-centric reinforce-ment learning (RL), imitation learning, and hybrid t

Cited by 1Source ↗code ↗Scholar ↗
2025

Few-Shot Neuro-Symbolic Imitation Learning for Long-Horizon Planning and Acting

Pierrick Lorang, Hong Lu, Johannes Huemer, Patrik Zips, Matthias Scheutz
CoRL 2025poster

Imitation learning enables intelligent systems to acquire complex behaviors with minimal supervision. However, existing methods often focus on short-horizon skills, require large datasets, and struggle to solve long-horizon tasks or generalize across task variations and distribution shifts. We propo…

Cited by 0Source ↗Scholar ↗
2025

Incremental Language Understanding for Online Motion Planning of Robot Manipulators

Mitchell Abrams, Thies Oelerich, Christian Hartl-Nesic, Andreas Kugi, Matthias Scheutz
IROS 2025

Human-robot interaction requires robots to process language incrementally, adapting their actions in real-time based on evolving speech input. Existing approaches to language-guided robot motion planning typically assume fully specified instructions, resulting in inefficient stop-and-replan behavior

Cited by 1Source ↗Scholar ↗
2024

A Framework for Neurosymbolic Goal-Conditioned Continual Learning in Open World Environments

Pierrick Lorang, Shivam Goel, Yash Shukla, Patrik Zips, Matthias Scheutz
IROS 2024poster

In dynamic open-world environments, agents continually face new challenges due to sudden and unpredictable novelties, hindering Task and Motion Planning (TAMP) in autonomous systems. We introduce a novel TAMP architecture that integrates symbolic planning with reinforcement learning to enable autono…

Cited by 1Source ↗Scholar ↗
2024

Adapting to the “Open World”: The Utility of Hybrid Hierarchical Reinforcement Learning and Symbolic Planning

Pierrick Lorang, Helmut Horvath, Tobias Kietreiber, Patrik Zips, Clemens Heitzinger, Matthias Scheutz
ICRA 2024poster

Open-world robotic tasks such as autonomous driving pose significant challenges to robot control due to unknown and unpredictable events that disrupt task performance. Neural network-based reinforcement learning (RL) techniques (like DQN, PPO, SAC, etc.) struggle to adapt in large domains and suffer…

Cited by 3Source ↗Scholar ↗
2024

Automating Dataset Production Using Generative Text and Image Models

Christopher Thierauf, Mitchell Abrams, Matthias Scheutz
COLING 2024main

Practical and ethical dataset collection remains a challenge blocking many empirical methods in natural language processing, resulting in a lack of benchmarks or data on which to test hypotheses. We propose a solution to some of these areas by presenting a pipeline to reduce the research burden of p…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2023

A Principled Approach to Model Validation in Domain Generalization

Boyang Lyu, Thuan Nguyen, Matthias Scheutz, Prakash Ishwar, Shuchin Aeron
ICASSP 2023accepted

Domain generalization aims to learn a model with good generalization ability, that is, the learned model should not only perform well on several seen domains but also on unseen domains with different data distributions. State-of-the-art domain generalization methods typically train a representation…

Cited by 0Source ↗Scholar ↗
2023

Investigating a Generalization of Probabilistic Material Implication and Bayesian Conditionals

Michael Jahn, Matthias Scheutz
UAI 2023poster

Probabilistic "if A then B" rules are typically formalized as Bayesian conditionals P(B|A), as many (e.g., Pearl) have argued that Bayesian conditionals are the correct way to think about such rules. However, there are challenges with standard inferences such as modus ponens and modus tollens that m…

Cited by 1Source ↗PDF ↗Scholar ↗
2021

Enabling Fast Instruction-Based Modification of Learned Robot Skills

Tyler Frasca, Bradley Oosterveld, Meia Chita-Tegmark, Matthias Scheutz
AAAI 2021technical

Much research effort in HRI has focused on how to enable robots to learn new skills from observations, demonstrations, and instructions. Less work, however, has focused on how skills can be corrected if they were learned incorrectly, adapted to changing circumstances, or generalized/specialized to d…

Cited by 12Source ↗PDF ↗Scholar ↗
2021

Robot Development and Path Planning for Indoor Ultraviolet Light Disinfection

Jonathan Conroy, Christopher Thierauf, Parker Rule, Evan Krause, Hugo Akitaya, Andrei Gonczi +2
ICRA 2021poster

Regular irradiation of indoor environments with ultraviolet C (UVC) light has become a regular task for many in-door settings as a result of COVID-19, but current robotic systems attempting to automate it suffer from high costs and inefficient irradiation. In this paper, we propose a purpose-made in…

Cited by 26Source ↗code ↗Scholar ↗
2020

Going Cognitive: A Demonstration of the Utility of Task-General Cognitive Architectures for Adaptive Robotic Task Performance

Tyler Frasca, Zhao Han, Jordan Allspaw, Holly Yanco, Matthias Scheutz
IROS 2020poster

It has been claimed that a main advantage of cognitive architectures (compared to other types of specialized robotic architectures) is that they are task-general and can thus learn to perform any task as long as they have the right perceptual and action primitives. In this paper, we provide empirica…

Cited by 6Source ↗Scholar ↗
2020

Reasoning Requirements for Indirect Speech Act Interpretation

Vasanth Sarathy, Alexander Tsuetaki, Antonio Roque, Matthias Scheutz
COLING 2020main

We perform a corpus analysis to develop a representation of the knowledge and reasoning used to interpret indirect speech acts. An indirect speech act (ISA) is an utterance whose intended meaning is different from its literal meaning. We focus on those speech acts in which slight changes in situatio…

Cited by 7Source ↗PDF ↗Scholar ↗
2019

Acquisition of Word-Object Associations from Human-Robot and Human-Human Dialogues

Sepideh Sadeghi, Brad Oosterveld, Evan Krause, Matthias Scheutz
ICRA 2019poster

Past work on acquisition of word-object associations in robots has focused on either fast instruction-based methods which accept highly constrained input or gradual cross-situational learning methods, but not a mixture of both. In this paper, we present an integrated robotic system which allows for…

Cited by 1Source ↗Scholar ↗
2017

A parallelized dynamic programming approach to zero resource spoken term discovery

Bradley Oosterveld, Richard Veale, Matthias Scheutz
ICASSP 2017accepted

Zero resource spoken term discovery in continuous speech is the discovery of repeated patterns in acoustic signals without any higher level linguistic information. These patterns are then combined to define the compositional units of that speech. We describe and implement an algorithm that tags simi…

Cited by 6Source ↗Scholar ↗
2017

Differences in interaction patterns and perception for teleoperated and autonomous humanoid robots

Maxwell Bennett, Tom Williams, Daria Thames, Matthias Scheutz
IROS 2017poster

As the linguistic capabilities of interactive robots advance, it becomes increasingly important to understand how humans will instruct robots through natural language. What is more, with the increased use of teleoperated humanoid robots, it is important to recognize whether any differences between i…

Cited by 44Source ↗Scholar ↗
2017

Resolution of Referential Ambiguity in Human-Robot Dialogue Using Dempster-Shafer Theoretic Pragmatics

Tom Williams, Matthias Scheutz
RSS 2017poster

Robots designed to interact with humans in realistic environments must be able to handle uncertainty with respect to the identities and properties of the people, places, and things found in their environments. When humans refer to these entities using under-specified language, robots must often gene…

Cited by 21Source ↗PDF ↗Scholar ↗
2015

Planning for serendipity

Tathagata Chakraborti, Gordon Briggs, Kartik Talamadupula, Yu Zhang, Matthias Scheutz, David Smith +1
IROS 2015poster

Recently there has been a lot of focus on human robot co-habitation issues that are often orthogonal to many aspects of human-robot teaming; e.g. on producing socially acceptable behaviors of robots and de-conflicting plans of robots and humans in shared environments. However, an interesting offshoo…

Cited by 60Source ↗Scholar ↗