27 citations · 42 across the 31 of their papers we have counts for
16 papers · 1 filter
Learning Spatial Bimanual Action Models Based on Affordance Regions and Human Demonstrations
Björn S. Plonka, Christian Dreher, Andre Meixner +2
In this paper, we present a novel approach for learning bimanual manipulation actions from human demonstration by extracting spatial constraints between affordance regions, termed…
Safe Reinforcement Learning of Robot Trajectories in the Presence of Moving Obstacles
Jonas Kiemel, Ludovic Righetti, Torsten Kröger +1
In this paper, we present an approach for learning collision-free robot trajectories in the presence of moving obstacles. As a first step, we train a backup policy to generate evas…
On Probabilistic Pullback Metrics for Latent Hyperbolic Manifolds
Luis Augenstein, Noémie Jaquier, Tamim Asfour +1
Probabilistic Latent Variable Models (LVMs) excel at modeling complex, high-dimensional data through lower-dimensional representations. Recent advances show that equipping these la…
A Riemannian Framework for Learning Reduced-order Lagrangian Dynamics
Katharina Friedl, Noémie Jaquier, Jens Lundell +2
By incorporating physical consistency as inductive bias, deep neural networks display increased generalization capabilities and data efficiency in learning nonlinear dynamic models…
Episodic Memory Verbalization using Hierarchical Representations of Life-Long Robot Experience
Leonard Bärmann, Chad DeChant, Joana Plewnia +4
Verbalization of robot experience, i.e., summarization of and question answering about a robot's past, is a crucial ability for improving human-robot interaction. Previous works ap…
Force Myography based Torque Estimation in Human Knee and Ankle Joints
Charlotte Marquardt, Arne Schulz, Miha Dezman +3
The online adaptation of exoskeleton control based on muscle activity sensing offers a promising approach to personalizing exoskeleton behavior based on the user's biosignals. Whil…
PerAct2: Benchmarking and Learning for Robotic Bimanual Manipulation Tasks
Markus Grotz, Mohit Shridhar, Tamim Asfour +1
Bimanual manipulation is challenging due to precise spatial and temporal coordination required between two arms. While there exist several real-world bimanual systems, there is a l…
Influence of Motion Restrictions in an Ankle Exoskeleton on Gait Kinematics and Stability in Straight Walking
Miha Dezman, Charlotte Marquardt, Adnan Ugur +1
Exoskeleton devices impose kinematic constraints on a user's motion and affect their stability due to added mass but also due to the simplified mechanical design. This paper invest…
SciEx: Benchmarking Large Language Models on Scientific Exams with Human Expert Grading and Automatic Grading
Tu Anh Dinh, Carlos Mullov, Leonard Bärmann +15
With the rapid development of Large Language Models (LLMs), it is crucial to have benchmarks which can evaluate the ability of LLMs on different domains. One common use of LLMs is…
Bi-KVIL: Keypoints-based Visual Imitation Learning of Bimanual Manipulation Tasks
Jianfeng Gao, Xiaoshu Jin, Franziska Krebs +2
Visual imitation learning has achieved impressive progress in learning unimanual manipulation tasks from a small set of visual observations, thanks to the latest advances in comput…
Visual Imitation Learning of Task-Oriented Object Grasping and Rearrangement
Yichen Cai, Jianfeng Gao, Christoph Pohl +1
Task-oriented object grasping and rearrangement are critical skills for robots to accomplish different real-world manipulation tasks. However, they remain challenging due to partia…
Learning Symbolic and Subsymbolic Temporal Task Constraints from Bimanual Human Demonstrations
Christian Dreher, Tamim Asfour
Learning task models of bimanual manipulation from human demonstration and their execution on a robot should take temporal constraints between actions into account. This includes c…
KITchen: A Real-World Benchmark and Dataset for 6D Object Pose Estimation in Kitchen Environments
Abdelrahman Younes, Tamim Asfour
Despite the recent progress on 6D object pose estimation methods for robotic grasping, a substantial performance gap persists between the capabilities of these methods on existing…
Riemannian Flow Matching Policy for Robot Motion Learning
Max Braun, Noémie Jaquier, Leonel Rozo +1
We introduce Riemannian Flow Matching Policies (RFMP), a novel model for learning and synthesizing robot visuomotor policies. RFMP leverages the efficient training and inference ca…
AutoGPT+P: Affordance-based Task Planning with Large Language Models
Timo Birr, Christoph Pohl, Abdelrahman Younes +1
Recent advances in task planning leverage Large Language Models (LLMs) to improve generalizability by combining such models with classical planning algorithms to address their inhe…
MAkEable: Memory-centered and Affordance-based Task Execution Framework for Transferable Mobile Manipulation Skills
Christoph Pohl, Fabian Reister, Fabian Peller-Konrad +1
To perform versatile mobile manipulation tasks in human-centered environments, the ability to efficiently transfer learned tasks and experiences from one robot to another or across…