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differentiable-particle-filters
differentiable-particle-filters PublicSource code and data for the paper "Differentiable Particle Filters: End-to-End Learning with Algorithmic Priors"
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turbo-ros-pkg
turbo-ros-pkg PublicROS software repository, Robotics & Biology Laboratory, TU Berlin
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learning-state-representations-with-robotic-priors
learning-state-representations-with-robotic-priors PublicCode and data accompaning the paper "Learning State Representations with Robotic Priors" (Jonschkowski and Brock, 2015).
Repositories
- CoupledParticleFilters Public
Code and affordance annotations for the Paper Coupled Particle Filters for Robust Affordance Estimation
- contact_graspnet_pytorch Public Forked from elchun/contact_graspnet_pytorch
Pytorch implementation of Contact-GraspNet
- tol-aicon Public
source code and description for the paper: No Plan, Yet Human: A Reactive Robotics Model Predicts Human Planning Failures on a Clinical Task
- RBO_dataset_affordances Public
Affordance annotations for the RBO dataset of articulated objects and interactions
- aicon Public
A framework for building adaptive robotic systems that dynamically adjust component interactions to estimate states and optimize actions using gradient descent, solving sequential tasks. Code for 'No Plan but Everything Under Control: Robustly Solving Sequential Tasks with Dynamically Composed Gradient Descent' (Mengers & Brock, ICRA25).
- differentiable-particle-filters Public
Source code and data for the paper "Differentiable Particle Filters: End-to-End Learning with Algorithmic Priors"
- omip Public
Framework for Online Interactive Perception (OMIP) for robots and system to perceive kinematic structures of articulated objects from interactions. For more info on the framework and the system see http://www.robotics.tu-berlin.de/fileadmin/fg170/Publikationen_pdf/martinmartin_ip_iros_2014.pdf
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