881 citations · 1.9k across the 36 of their papers we have counts for
29 papers · 1 filter
Transfer Learning in Robotics: An Upcoming Breakthrough? A Review of Promises and Challenges
Noémie Jaquier, Michael C. Welle, Andrej Gams +6
Transfer learning is a conceptually-enticing paradigm in pursuit of truly intelligent embodied agents. The core concept -- reusing prior knowledge to learn in and from novel situat…
Incremental Learning of Humanoid Robot Behavior from Natural Interaction and Large Language Models
Leonard Bärmann, Rainer Kartmann, Fabian Peller-Konrad +3
Natural-language dialog is key for intuitive human-robot interaction. It can be used not only to express humans' intents, but also to communicate instructions for improvement if a…
Uncertainty-aware Risk Assessment of Robotic Systems via Importance Sampling
Woo-Jeong Baek, Tom P. Huck, Joschka Haas +3
In this paper, we introduce a probabilistic approach to risk assessment of robot systems by focusing on the impact of uncertainties. While various approaches to identifying systema…
On the Design of Region-Avoiding Metrics for Collision-Safe Motion Generation on Riemannian Manifolds
Holger Klein, Noémie Jaquier, Andre Meixner +1
The generation of energy-efficient and dynamic-aware robot motions that satisfy constraints such as joint limits, self-collisions, and collisions with the environment remains a cha…
Interactive and Incremental Learning of Spatial Object Relations from Human Demonstrations
Rainer Kartmann, Tamim Asfour
Humans use semantic concepts such as spatial relations between objects to describe scenes and communicate tasks such as "Put the tea to the right of the cup" or "Move the plate bet…
Virtual Reality via Object Pose Estimation and Active Learning: Realizing Telepresence Robots with Aerial Manipulation Capabilities
Jongseok Lee, Ribin Balachandran, Konstantin Kondak +6
This article presents a novel telepresence system for advancing aerial manipulation in dynamic and unstructured environments. The proposed system not only features a haptic device,…
Bringing motion taxonomies to continuous domains via GPLVM on hyperbolic manifolds
Noémie Jaquier, Leonel Rozo, Miguel González-Duque +2
Human motion taxonomies serve as high-level hierarchical abstractions that classify how humans move and interact with their environment. They have proven useful to analyse grasps,…
Riemannian geometry as a unifying theory for robot motion learning and control
Noémie Jaquier, Tamim Asfour
Riemannian geometry is a mathematical field which has been the cornerstone of revolutionary scientific discoveries such as the theory of general relativity. Despite early uses in r…
SpeedFolding: Learning Efficient Bimanual Folding of Garments
Yahav Avigal, Lars Berscheid, Tamim Asfour +2
Folding garments reliably and efficiently is a long standing challenge in robotic manipulation due to the complex dynamics and high dimensional configuration space of garments. An…
K-VIL: Keypoints-based Visual Imitation Learning
Jianfeng Gao, Zhi Tao, Noémie Jaquier +1
Visual imitation learning provides efficient and intuitive solutions for robotic systems to acquire novel manipulation skills. However, simultaneously learning geometric task const…
A Riemannian Take on Human Motion Analysis and Retargeting
Holger Klein, Noémie Jaquier, Andre Meixner +1
Dynamic motions of humans and robots are widely driven by posture-dependent nonlinear interactions between their degrees of freedom. However, these dynamical effects remain mostly…
Deep Learning Approaches to Grasp Synthesis: A Review
Rhys Newbury, Morris Gu, Lachlan Chumbley +9
Grasping is the process of picking up an object by applying forces and torques at a set of contacts. Recent advances in deep-learning methods have allowed rapid progress in robotic…
Learning to Sequence and Blend Robot Skills via Differentiable Optimization
Noémie Jaquier, You Zhou, Julia Starke +1
In contrast to humans and animals who naturally execute seamless motions, learning and smoothly executing sequences of actions remains a challenge in robotics. This paper introduce…
Geometry-aware Bayesian Optimization in Robotics using Riemannian Matérn Kernels
Noémie Jaquier, Viacheslav Borovitskiy, Andrei Smolensky +3
Bayesian optimization is a data-efficient technique which can be used for control parameter tuning, parametric policy adaptation, and structure design in robotics. Many of these pr…
Graph-based Task-specific Prediction Models for Interactions between Deformable and Rigid Objects
Zehang Weng, Fabian Paus, Anastasiia Varava +3
Capturing scene dynamics and predicting the future scene state is challenging but essential for robotic manipulation tasks, especially when the scene contains both rigid and deform…
Learning to Shift Attention for Motion Generation
You Zhou, Jianfeng Gao, Tamim Asfour
One challenge of motion generation using robot learning from demonstration techniques is that human demonstrations follow a distribution with multiple modes for one task query. Pre…
Object and Relation Centric Representations for Push Effect Prediction
Ahmet E. Tekden, Aykut Erdem, Erkut Erdem +2
Pushing is an essential non-prehensile manipulation skill used for tasks ranging from pre-grasp manipulation to scene rearrangement, reasoning about object relations in the scene,…
Uncertainty-aware Contact-safe Model-based Reinforcement Learning
Cheng-Yu Kuo, Andreas Schaarschmidt, Yunduan Cui +2
This letter presents contact-safe Model-based Reinforcement Learning (MBRL) for robot applications that achieves contact-safe behaviors in the learning process. In typical MBRL, we…
A Soft Humanoid Hand with In-Finger Visual Perception
Felix Hundhausen, Julia Starke, Tamim Asfour
We present a novel underactued humanoid five finger soft hand, the KIT \softhand, which is equipped with cameras in the fingertips and integrates a high performance embedded system…
Learning Compliance Adaptation in Contact-Rich Manipulation
Jianfeng Gao, You Zhou, Tamim Asfour
Compliant robot behavior is crucial for the realization of contact-rich manipulation tasks. In such tasks, it is important to ensure a high stiffness and force tracking accuracy du…
Learning Object-Action Relations from Bimanual Human Demonstration Using Graph Networks
Christian R. G. Dreher, Mirko Wächter, Tamim Asfour
Recognizing human actions is a vital task for a humanoid robot, especially in domains like programming by demonstration. Previous approaches on action recognition primarily focused…
A Framework for Evaluating Motion Segmentation Algorithms
Christian R. G. Dreher, Nicklas Kulp, Christian Mandery +2
There have been many proposals for algorithms segmenting human whole-body motion in the literature. However, the wide range of use cases, datasets, and quality measures that were u…
Planning High-Quality Grasps using Mean Curvature Object Skeletons
Nikolaus Vahrenkamp, Eduard Koch, Mirko Waechter +1
In this work, we present a grasp planner which integrates two sources of information to generate robust grasps for a robotic hand. First, the topological information of the object…
Multimodal Gaze Stabilization of a Humanoid Robot based on Reafferences
Timothee Habra, Markus Grotz, David Sippel +2
Gaze stabilization is fundamental for humanoid robots. By stabilizing vision, it enhances perception of the environment and keeps points of interest in the field of view. In this c…