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On the Importance of Tactile Sensing for Imitation Learning: A Case Study on Robotic Match Lighting
The field of robotic manipulation has advanced significantly in the last years. At the sensing level, several novel tactile sensors …
Niklas Funk, Changqi Chen, Tim Schneider, Georgia Chalvatzaki, Roberto Calandra, Jan Peters
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On the Importance of Tactile Sensing for Imitation Learning: A Case Study on Robotic Match Lighting
Particle-based 6D Object Pose Estimation from Point Clouds using Diffusion Models
Object pose estimation from a single view remains a challenging problem. In particular, partial observability, occlusions, and object …
Christian Moeller, Niklas Funk, Jan Peters
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Learning Force Distribution Estimation for the GelSight Mini Optical Tactile Sensor based on Finite Element Analysis
Contact-rich manipulation remains a major challenge in robotics. Optical tactile sensors like GelSight Mini offer a low-cost solution …
Erik Helmut, Luca Dziarski, Niklas Funk, Boris Belousov, Jan Peters
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ActionFlow: Equivariant, Accurate, and Efficient Policies with Spatially Symmetric Flow Matching
Best Paper Award in the RSS 2024 Workshop on Priors4Robots This paper introduces a novel policy class combining Flow Matching with SE(3) Invariant Transformers for efficient, equivariant, and expressive robot learning from demonstrations. We showcase the performance of the method across simulation and real robot environment considering low dimensional as well as high dimensional real-world observations.
Niklas Funk, Julen Urain, Joao Carvalho, Vignesh Prasad, Georgia Chalvatzaki, Jan Peters
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ActionFlow: Equivariant, Accurate, and Efficient Policies with Spatially Symmetric Flow Matching