Search Results for author:
Found 7 papers, 3 papers with code
Bridging the gap between Learning-to-plan, Motion Primitives and Safe Reinforcement Learning
We evaluate our approach against state-of-the-art safe reinforcement learning methods, showing that our technique, particularly when exploiting task structure, outperforms baseline methods in challenging scenarios such as planning to hit in robot air hockey.
Learning Quasi-Static 3D Models of Markerless Deformable Linear Objects for Bimanual Robotic Manipulation
This paper analyzes several learning-based 3D models of the DLO and proposes a new one based on the Transformer architecture that achieves superior accuracy, even on the DLOs of different lengths, thanks to the proposed scaling method.
DLOFTBs -- Fast Tracking of Deformable Linear Objects with B-splines
While manipulating rigid objects is an extensively explored research topic, deformable linear object (DLO) manipulation seems significantly underdeveloped.
Fast Kinodynamic Planning on the Constraint Manifold with Deep Neural Networks
Motion planning is a mature area of research in robotics with many well-established methods based on optimization or sampling the state space, suitable for solving kinematic motion planning.
Gaining a Sense of Touch. Physical Parameters Estimation using a Soft Gripper and Neural Networks
A crucial problem is to estimate the physical parameters of a squeezed object to adjust the manipulation procedure, which is considered as a significant challenge.
A Summary of the 4th International Workshop on Recovering 6D Object Pose
The workshop featured four invited talks, oral and poster presentations of accepted workshop papers, and an introduction of the BOP benchmark for 6D object pose estimation.
A Hierarchical Approach for Joint Multi-view Object Pose Estimation and Categorization
We propose a joint object pose estimation and categorization approach which extracts information about object poses and categories from the object parts and compositions constructed at different layers of a hierarchical object representation algorithm, namely Learned Hierarchy of Parts (LHOP).
