3 papers
cs.RO2025
Context-Based Meta Reinforcement Learning for Robust and Adaptable Peg-in-Hole Assembly Tasks
Ahmed Shokry, Walid Gomaa, Tobias Zaenker +5
Autonomous assembly is an essential capability for industrial and service robots, with Peg-in-Hole (PiH) insertion being one of the core tasks. However, PiH assembly in unknown env…
cs.RO2025
GO-VMP: Global Optimization for View Motion Planning in Fruit Mapping
Allen Isaac Jose, Sicong Pan, Tobias Zaenker +3
Automating labor-intensive tasks such as crop monitoring with robots is essential for enhancing production and conserving resources. However, autonomously monitoring horticulture c…
cs.RO2025
Map Space Belief Prediction for Manipulation-Enhanced Mapping
Joao Marcos Correia Marques, Nils Dengler, Tobias Zaenker +4
Searching for objects in cluttered environments requires selecting efficient viewpoints and manipulation actions to remove occlusions and reduce uncertainty in object locations, sh…