@inproceedings{Rueckert2015,

title = {Extracting Low-Dimensional Control Variables for Movement Primitives},

author = {Elmar Rueckert and Jan Mundo and Alexandros Paraschos and Jan Peters and Gerhard Neumann},

url = {https://cps.unileoben.ac.at/wp/ICRA2015Rueckert.pdf, Article File

https://cps.unileoben.ac.at/wp/resources/code/MATLAB_LatentManifoldPrimitives_2015Rueckert.zip, MATALB Code

https://cps.unileoben.ac.at/wp/resources/videos/KUKA_LatentManifold_ProMPs_REAL_Elmar.mp4, Supplementary Video},

year  = {2015},

date = {2015-01-01},

booktitle = {Proceedings of the International Conference on Robotics and Automation (ICRA)},

crossref = {p10796},

key = {3rdhand, codyco},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Rueckert2019c,

title = {Dynamic Control Strategies for Cable-Driven Master Slave Robots},

author = {Elmar Rueckert and Philipp Jauer and Alexander Derksen and Achim Schweikard},

editor = {Tobias Keck},

doi = {10.18416/MIC.2019.1901007},

year  = {2019},

date = {2019-01-24},

booktitle = {Proceedings on Minimally Invasive Surgery, Luebeck, Germany},

note = {January 24-25, 2019},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Rottmann2020HiBO,

title = {Learning Hierarchical Acquisition Functions for Bayesian Optimization},

author = {N. Rottmann and T. Kunavar and J. Babič and J. Peters and E. Rueckert},

url = {https://cps.unileoben.ac.at/wp/IROS2020Rottmann.pdf, Article File},

year  = {2020},

date = {2020-10-25},

booktitle = {International Conference on Intelligent Robots and Systems (IROS’ 2020)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Xue2020,

title = {Sample-Efficient Covariance Matrix Adaptation Evolutional Strategy via Simulated Rollouts in Neural Networks},

author = {H. Xue and S. Boettger and N. Rottmann and H. Pandya and R. Bruder and G. Neumann and A. Schweikard and E. Rueckert},

url = {https://cps.unileoben.ac.at/wp/ASPAI2020Xue.pdf, Article File},

year  = {2020},

date = {2020-06-30},

booktitle = {International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI’ 2020)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Lygerakis2024,

title = {M2CURL: Sample-Efficient Multimodal Reinforcement Learning via Self-Supervised Representation Learning for Robotic Manipulation},

author = {Fotios Lygerakis and Vedant Dave and Elmar Rueckert},

url = {https://cloud.cps.unileoben.ac.at/index.php/s/NPejb2Fp4Y8LeyZ},

year  = {2024},

date = {2024-04-04},

urldate = {2024-04-04},

booktitle = {IEEE International Conference on Ubiquitous Robots (UR 2024)},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Dave2024b,

title = {Multimodal Visual-Tactile Representation Learning through Self-Supervised Contrastive Pre-Training},

author = {Vedant Dave* and Fotios Lygerakis* and Elmar Rueckert},

url = {https://cloud.cps.unileoben.ac.at/index.php/s/Nw9TprfdDoLgr8e},

year  = {2024},

date = {2024-01-28},

urldate = {2024-01-28},

booktitle = {IEEE International Conference on Robotics and Automation (ICRA 2024).},

note = {* equal contribution},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Dave2022,

title = {Predicting full-arm grasping motions from anticipated tactile responses},

author = {Vedant Dave and Elmar Rueckert},

url = {https://cloud.cps.unileoben.ac.at/index.php/s/WzGSNtc5WRLN3EL},

year  = {2022},

date = {2022-09-26},

urldate = {2022-09-26},

publisher = {International Conference on Humanoid Robots (Humanoids 2022)},

abstract = {Tactile sensing provides significant information about the state of the environment for performing manipulation tasks. Depending on the physical properties of the object, manipulation tasks can exhibit large variation in their movements. For a grasping task, the movement of the arm and of the end effector varies depending on different points of contact on the object, especially if the object is non-homogeneous in hardness and/or has an uneven geometry. In this paper, we propose Tactile Probabilistic Movement Primitives (TacProMPs), to learn a highly non-linear relationship between the desired tactile responses and the full-arm movement. We solely condition on the tactile responses to infer the complex manipulation skills. We formulate a joint trajectory of full-arm joints with tactile data, leverage the model to condition on the desired tactile response from the non-homogeneous object and infer the full-arm (7-dof panda arm and 19-dof gripper hand) motion. We use a Gaussian Mixture Model of primitives to address the multimodality in demonstrations. We also show that the measurement noise adjustment must be taken into account due to multiple systems working in collaboration.

We validate and show the robustness of the approach through two experiments. First, we consider an object with non-uniform hardness. Grasping from different locations require different motion, and results into different tactile responses. Second, we have an object with homogeneous hardness, but we grasp it with widely varying grasping configurations. Our result shows that TacProMPs can successfully model complex multimodal skills and generalise to new situations.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Leonel2022,

title = {Orientation Probabilistic Movement Primitives on Riemannian Manifolds},

author = {Leonel, Rozo* and Vedant, Dave*},

url = {https://cps.unileoben.ac.at/wp/orientation_probabilistic_move.pdf, Article File},

year  = {2022},

date = {2022-01-11},

urldate = {2022-01-11},

booktitle = {Conference on Robot Learning (CoRL)},

volume = {164},

pages = {11},

abstract = {Learning complex robot motions necessarily demands to have models that are able to encode and retrieve full-pose trajectories when tasks are defined in operational spaces. Probabilistic movement primitives (ProMPs) stand out as a principled approach that models trajectory distributions learned from demonstrations. ProMPs allow for trajectory modulation and blending to achieve better generalization to novel situations. However, when ProMPs are employed in operational space, their original formulation does not directly apply to full-pose movements including rotational trajectories described by quaternions. This paper proposes a Riemannian formulation of ProMPs that enables encoding and retrieving of quaternion trajectories. Our method builds on Riemannian manifold theory,

and exploits multilinear geodesic regression for estimating the ProMPs parameters. This novel approach makes ProMPs a suitable model for learning complex full-pose robot motion patterns. Riemannian ProMPs are tested on toy examples to illustrate their workflow, and on real learning-from-demonstration experiments.},

note = {* equal contribution},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Nwankwo2024,

title = {The Conversation is the Command: Interacting with Real-World Autonomous Robots Through Natural Language},

author = {Linus Nwankwo and Elmar Rueckert},

url = {https://doi.org/10.1145/3610978.3640723

https://cloud.cps.unileoben.ac.at/index.php/s/YzJdHWDt9ZdqsZs},

doi = {10.1145/3610978.3640723},

isbn = {9798400703232},

year  = {2024},

date = {2024-01-16},

urldate = {2024-01-16},

booktitle = {HRI '24: Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction.},

pages = {808–812},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

organization = {ACM/IEEE},

series = {HRI '24},

abstract = {In recent years, autonomous agents have surged in real-world environments such as our homes, offices, and public spaces. However, natural human-robot interaction remains a key challenge. In this paper, we introduce an approach that synergistically exploits the capabilities of large language models (LLMs) and multimodal vision-language models (VLMs) to enable humans to interact naturally with autonomous robots through conversational dialogue. We leveraged the LLMs to decode the high-level natural language instructions from humans and abstract them into precise robot actionable commands or queries. Further, we utilised the VLMs to provide a visual and semantic understanding of the robot's task environment. Our results with 99.13% command recognition accuracy and 97.96% commands execution success show that our approach can enhance human-robot interaction in real-world applications. The video demonstrations of this paper can be found at https://osf.io/wzyf6 and the code is available at our GitHub repository.},

howpublished = {ACM/IEEE International Conference on Human-Robot Interaction (HRI ’24 Companion)},

key = {ChatGPT, LLMs, ROS, VLMs, autonomous robots, human-robot interaction, natural language interaction},

note = {Published as late breaking results. Supplementary video: https://cloud.cps.unileoben.ac.at/index.php/s/fRE9XMosWDtJ339 },

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Nwankwo2023,

title = {Understanding why SLAM algorithms fail in modern indoor environments},

author = {Linus Nwankwo and Elmar Rueckert},

url = {https://cloud.cps.unileoben.ac.at/index.php/s/KdZ2E2np5QEnYfL

},

doi = {https://doi.org/10.1007/978-3-031-32606-6_22},

year  = {2023},

date = {2023-05-27},

urldate = {2023-05-27},

booktitle = {International Conference on Robotics in Alpe-Adria-Danube Region (RAAD). },

volume = {135},

pages = {186 - 194},

publisher = {Cham: Springer Nature Switzerland.},

series = {Mechanisms and Machine Science},

abstract = {Simultaneous localization and mapping (SLAM) algorithms are essential for the autonomous navigation of mobile robots. With the increasing demand for autonomous systems, it is crucial to evaluate and compare the performance of these algorithms in real-world environments. 

In this paper, we provide an evaluation strategy and real-world datasets to test and evaluate SLAM algorithms in complex and challenging indoor environments. Further, we analysed state-of-the-art (SOTA) SLAM algorithms based on various metrics such as absolute trajectory error, scale drift, and map accuracy and consistency. Our results demonstrate that SOTA SLAM algorithms often fail in challenging environments, with dynamic objects, transparent and reflecting surfaces. We also found that successful loop closures had a significant impact on the algorithm’s performance. These findings highlight the need for further research to improve the robustness of the algorithms in real-world scenarios. },

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Feith2024A,

title = {Integrating Human Expertise in Continuous Spaces: A Novel Interactive Bayesian Optimization Framework with Preference Expected Improvement},

author = {Nikolaus Feith and Elmar Rueckert},

url = {https://cloud.cps.unileoben.ac.at/index.php/s/6rTWAkoXa3zsJxf},

year  = {2024},

date = {2024-04-04},

urldate = {2024-04-04},

booktitle = {IEEE International Conference on Ubiquitous Robots (UR 2024)},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Feith2024B,

title = {Advancing Interactive Robot Learning: A User Interface Leveraging Mixed Reality and Dual Quaternions},

author = {Nikolaus Feith and Elmar Rueckert},

url = {https://cloud.cps.unileoben.ac.at/index.php/s/qCgzbmCJYSH3F97},

year  = {2024},

date = {2024-04-04},

urldate = {2024-04-04},

booktitle = {IEEE International Conference on Ubiquitous Robots (UR 2024)},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Tanneberg2017a,

title = {Efficient Online Adaptation with Stochastic Recurrent Neural Networks},

author = {Daniel Tanneberg and Jan Peters and Elmar Rueckert},

url = {https://cps.unileoben.ac.at/wp/Humanoids2017Tanneberg.pdf, Article File},

year  = {2017},

date = {2017-11-15},

booktitle = {Proceedings of the International Conference on Humanoid Robots (HUMANOIDS)},

crossref = {p11092},

key = {goal-robots, skills4robots},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Thiem2017b,

title = {Simulation of the underactuated Sake Robotics Gripper in V-REP},

author = {Simon Thiem and Svenja Stark and Daniel Tanneberg and Jan Peters and Elmar Rueckert},

url = {https://cps.unileoben.ac.at/wp/Humanoids2017Thiem.pdf},

year  = {2017},

date = {2017-11-15},

booktitle = {Workshop at the International Conference on Humanoid Robots (HUMANOIDS)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Tanneberg2017,

title = {Online Learning with Stochastic Recurrent Neural Networks using Intrinsic Motivation Signals},

author = {Daniel Tanneberg and Jan Peters and Elmar Rueckert},

url = {https://cps.unileoben.ac.at/wp/CoRL2017Tanneberg.pdf, Article File},

year  = {2017},

date = {2017-11-10},

booktitle = {Proceedings of the Conference on Robot Learning (CoRL)},

crossref = {p11088},

key = {goal-robots, skills4robots},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{tanneberg_humanoids16,

title = {Deep Spiking Networks for Model-based Planning in Humanoids},

author = {Daniel Tanneberg and Alexandros Paraschos and Jan Peters and Elmar Rueckert},

url = {https://cps.unileoben.ac.at/wp/Humanoids2016Tanneberg.pdf, Article File

https://cps.unileoben.ac.at/wp/resources/videos/humanoids_slower_540p.mp4, Supplementary Video},

year  = {2016},

date = {2016-11-16},

booktitle = {Proceedings of the International Conference on Humanoid Robots (HUMANOIDS)},

crossref = {p10980},

key = {codyco and tacman},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Sharma2016,

title = {Adaptive Training Strategies for BCIs},

author = {David Sharma and Daniel Tanneberg and Moritz Grosse-Wentrup and Jan Peters and Elmar Rueckert},

url = {https://cps.unileoben.ac.at/wp/Cybathlon2016Sharma.pdf, Article File},

year  = {2016},

date = {2016-01-01},

booktitle = {Cybathlon Symposium},

crossref = {p10952},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Stark2019,

title = {Experience Reuse with Probabilistic Movement Primitives},

author = {Svenja Stark and Jan Peters and Elmar Rueckert},

url = {https://cps.unileoben.ac.at/wp/IROS2019Stark.pdf, Article File},

year  = {2019},

date = {2019-11-03},

booktitle = {Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS), 2019.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Humanoids2017Stark,

title = {A Comparison of Distance Measures for Learning Nonparametric Motor Skill Libraries},

author = {Svenja Stark and Jan Peters and Elmar Rueckert},

url = {https://cps.unileoben.ac.at/wp/Humanoids2017Stark.pdf, Article File},

year  = {2017},

date = {2017-11-15},

booktitle = {Proceedings of the International Conference on Humanoid Robots (HUMANOIDS)},

crossref = {p11093},

key = {goal-robots, skills4robots},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Thiem2017b,

title = {Simulation of the underactuated Sake Robotics Gripper in V-REP},

author = {Simon Thiem and Svenja Stark and Daniel Tanneberg and Jan Peters and Elmar Rueckert},

url = {https://cps.unileoben.ac.at/wp/Humanoids2017Thiem.pdf},

year  = {2017},

date = {2017-11-15},

booktitle = {Workshop at the International Conference on Humanoid Robots (HUMANOIDS)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Xue2022b,

title = {End-To-End Deep Reinforcement Learning for First-Person Pedestrian Visual Navigation in Urban Environments},

author = {Honghu Xue and Rui Song and Julian Petzold and Benedikt Hein and Heiko Hamann and Elmar Rueckert},

url = {https://cloud.cps.unileoben.ac.at/index.php/s/RzMQWqsFarQ6Kw4},

year  = {2022},

date = {2022-09-26},

urldate = {2022-09-26},

publisher = {International Conference on Humanoid Robots (Humanoids 2022)},

abstract = {We solve a visual navigation problem in an urban setting via deep reinforcement learning in an end-to-end manner. A major challenge of a first-person visual navigation problem lies in severe partial observability and sparse positive experiences of reaching the goal. To address partial observability, we propose a novel 3D-temporal convolutional network to encode sequential historical visual observations, its effectiveness is verified by comparing to a commonly-used frame-stacking

approach. For sparse positive samples, we propose an improved automatic curriculum learning algorithm NavACL+, which

proposes meaningful curricula starting from easy tasks and gradually generalizes to challenging ones. NavACL+ is shown to

facilitate the learning process, greatly improve the task success rate on difficult tasks by at least 40% and offer enhanced

generalization to different initial poses compared to training from a fixed initial pose and the original NavACL algorithm.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{nokey,

title = {ACNMP: Skill Transfer and Task Extrapolation through Learning from Demonstration and Reinforcement Learning via Representation Sharing},

author = {M Tuluhan Akbulut and Erhan Oztop and M Yunus Seker and Honghu Xue and Ahmet E Tekden and Emre Ugur},

url = {https://cps.unileoben.ac.at/wp/CoRL2020Akbulut.pdf},

year  = {2020},

date = {2020-11-20},

urldate = {2020-11-20},

abstract = {To equip robots with dexterous skills, an effective approach is to first transfer the desired skill via Learning from Demonstration (LfD), then let the robot improve it by self-exploration via Reinforcement Learning (RL). In this paper, we propose a novel LfD+RL framework, namely Adaptive Conditional Neural Movement Primitives (ACNMP), that allows efficient policy improvement in novel environments and effective skill transfer between different agents. This is achieved through exploiting the latent representation learned by the underlying Conditional Neural Process (CNP) model, and simultaneous training of the model with supervised learning (SL) for acquiring the demonstrated trajectories and via RL for new trajectory discovery. Through simulation experiments, we show that (i) ACNMP enables the system to extrapolate to situations where pure LfD fails; (ii) Simultaneous training of the system through SL and RL preserves the shape of demonstrations while adapting to novel situations due to the shared representations used by both learners; (iii) ACNMP enables order-of-magnitude sample-efficient RL in extrapolation of reaching tasks compared to the existing approaches; (iv) ACNMPs can be used to implement skill transfer between robots having different morphology, with competitive learning speeds and importantly with less number of assumptions compared to the state-of-the-art approaches. Finally, we show the real-world suitability of ACNMPs through real robot experiments that involve obstacle avoidance, pick and place and pouring actions.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Rottmann2020c,

title = {Parameter Optimization for Loop Closure Detection in Closed Environments},

author = {N. Rottmann and R. Bruder and H. Xue and A. Schweikard and E. Rueckert},

url = {https://cps.unileoben.ac.at/wp/IROSWS2020Rottmann.pdf, Article File},

year  = {2020},

date = {2020-10-25},

booktitle = {Workshop Paper at the International Conference on Intelligent Robots and Systems (IROS)},

pages = {1--8},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Xue2020,

title = {Sample-Efficient Covariance Matrix Adaptation Evolutional Strategy via Simulated Rollouts in Neural Networks},

author = {H. Xue and S. Boettger and N. Rottmann and H. Pandya and R. Bruder and G. Neumann and A. Schweikard and E. Rueckert},

url = {https://cps.unileoben.ac.at/wp/ASPAI2020Xue.pdf, Article File},

year  = {2020},

date = {2020-06-30},

booktitle = {International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI’ 2020)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}