1 code implementation • 5 Jun 2024 • Alan A. Lahoud, Erik Schaffernicht, Johannes A. Stork
We propose a novel framework that models prediction uncertainty with Bayesian Neural Networks (BNNs) and propagates this uncertainty into the mathematical solver with a Stochastic Programming technique.
1 code implementation • 8 May 2024 • Alan A. Lahoud, Erik Schaffernicht, Johannes A. Stork
Learning latent costs of transitions on graphs from trajectories demonstrations under various contextual features is challenging but useful for path planning.
no code implementations • 2 May 2024 • Finn Rietz, Erik Schaffernicht, Stefan Heinrich, Johannes A. Stork
Reinforcement learning policies are typically represented by black-box neural networks, which are non-interpretable and not well-suited for safety-critical domains.
no code implementations • 26 Oct 2023 • Shih-Min Yang, Martin Magnusson, Johannes A. Stork, Todor Stoyanov
We solve this problem by learning a sequence of actions that utilize the environment to change the object's pose.
no code implementations • 20 Sep 2022 • Finn Rietz, Erik Schaffernicht, Todor Stoyanov, Johannes A. Stork
Combining learned policies in a prioritized, ordered manner is desirable because it allows for modular design and facilitates data reuse through knowledge transfer.
no code implementations • 19 Sep 2022 • Quantao Yang, Johannes A. Stork, Todor Stoyanov
We propose to learn prior distribution over the specific skill required to accomplish each task and compose the family of skill priors to guide learning the policy for a new task by comparing the similarity between the target task and the prior ones.
no code implementations • 13 May 2020 • Isac Arnekvist, J. Frederico Carvalho, Danica Kragic, Johannes A. Stork
To further investigate this matter, we analyze a discrete-time linear autonomous system, and show theoretically how this relates to a model with a single ReLU and how common properties can result in dying ReLU.
no code implementations • 12 Feb 2020 • Johannes A. Stork, Todor Stoyanov
In this paper, we learn a compact and continuous implicit surface map of an environment from a stream of range data with known poses.
no code implementations • 10 Oct 2018 • Rika Antonova, Mia Kokic, Johannes A. Stork, Danica Kragic
Our further contribution is a neural network architecture and training pipeline that use experience from grasping objects in simulation to learn grasp stability scores.
no code implementations • 12 Sep 2018 • Weihao Yuan, Kaiyu Hang, Haoran Song, Danica Kragic, Michael Y. Wang, Johannes A. Stork
Moving a human body or a large and bulky object can require the strength of whole arm manipulation (WAM).
no code implementations • 10 Sep 2018 • Isac Arnekvist, Danica Kragic, Johannes A. Stork
The low-dimensional space, and master policy found by our method enables policies to quickly adapt to new environments.
no code implementations • 15 Mar 2018 • Weihao Yuan, Johannes A. Stork, Danica Kragic, Michael Y. Wang, Kaiyu Hang
Usually, this is achieved by precisely modeling physical properties of the objects, robot, and the environment for explicit planning.