no code implementations • 10 Apr 2024 • Andrej Kruzliak, Jiri Hartvich, Shubhan P. Patni, Lukas Rustler, Jan Kristof Behrens, Fares J. Abu-Dakka, Krystian Mikolajczyk, Ville Kyrki, Matej Hoffmann
The robot pipeline integrates with a logging module and an online database of objects, containing over 24, 000 measurements of 63 objects with different grippers.
no code implementations • 22 Mar 2024 • Daulet Baimukashev, Gokhan Alcan, Ville Kyrki
Inverse reinforcement learning (IRL) is an imitation learning approach to learning reward functions from expert demonstrations.
no code implementations • 5 Mar 2024 • Shibei Zhu, Tran Nguyen Le, Samuel Kaski, Ville Kyrki
We consider a type of collaboration in a shared-autonomy fashion, where both a human operator and an assistive robot act simultaneously in the same task space that affects each other's actions.
no code implementations • 9 Jan 2024 • Oskar Keurulainen, Gokhan Alcan, Ville Kyrki
Building machines capable of efficiently collaborating with humans has been a longstanding goal in artificial intelligence.
no code implementations • 30 Oct 2023 • Farzeen Munir, Shoaib Azam, Tomasz Kucner, Ville Kyrki, Moongu Jeon
This underscores the value of radar-Lidar fusion in achieving precise object detection and localization, especially in challenging weather conditions.
no code implementations • 17 Oct 2023 • Quentin Bouniot, Ievgen Redko, Anton Mallasto, Charlotte Laclau, Karol Arndt, Oliver Struckmeier, Markus Heinonen, Ville Kyrki, Samuel Kaski
The remarkable success of deep neural networks (DNN) is often attributed to their high expressive power and their ability to approximate functions of arbitrary complexity.
no code implementations • 14 Oct 2023 • David Blanco-Mulero, Oriol Barbany, Gokhan Alcan, Adrià Colomé, Carme Torras, Ville Kyrki
The dataset is collected by performing a dynamic as well as a quasi-static cloth manipulation task involving contact with a rigid table.
1 code implementation • 12 May 2023 • Oliver Struckmeier, Ievgen Redko, Anton Mallasto, Karol Arndt, Markus Heinonen, Ville Kyrki
Optimal transport (OT) is a powerful geometric tool used to compare and align probability measures following the least effort principle.
no code implementations • 23 Mar 2023 • David Blanco-Mulero, Gokhan Alcan, Fares J. Abu-Dakka, Ville Kyrki
To address this challenge, we introduce the Quasi-Dynamic Parameterisable (QDP) method, which optimises parameters such as the motion velocity in addition to the pick and place positions of quasi-static and dynamic manipulation primitives.
no code implementations • 5 Jan 2023 • Gokhan Alcan, Fares J. Abu-Dakka, Ville Kyrki
Matrix Lie groups are an important class of manifolds commonly used in control and robotics, and the optimization of control policies on these manifolds is a fundamental problem.
no code implementations • 13 Oct 2022 • Shoaib Azam, Farzeen Munir, Ville Kyrki, Moongu Jeon, Witold Pedrycz
Recent perception systems enhance spatial understanding with sensor fusion but often lack full environmental context.
no code implementations • 2 Sep 2022 • Chang Rajani, Karol Arndt, David Blanco-Mulero, Kevin Sebastian Luck, Ville Kyrki
To this end we propose a co-imitation methodology for adapting behaviour and morphology by matching state distributions of the demonstrator.
1 code implementation • 23 Aug 2022 • Francesco Verdoja, Tomasz Piotr Kucner, Ville Kyrki
Moreover, approaches for mapping dynamics are unable to transfer the learned models across environments: each model is only able to describe the dynamics of the environment it has been built in.
1 code implementation • 29 Jun 2022 • Gabriele Tiboni, Karol Arndt, Giuseppe Averta, Ville Kyrki, Tatiana Tommasi
However, transferring the acquired knowledge to the real world can be challenging due to the reality gap.
no code implementations • 18 Apr 2022 • Ali Ghadirzadeh, Petra Poklukar, Karol Arndt, Chelsea Finn, Ville Kyrki, Danica Kragic, Mårten Björkman
We present a data-efficient framework for solving sequential decision-making problems which exploits the combination of reinforcement learning (RL) and latent variable generative models.
1 code implementation • 17 Mar 2022 • Lukas Rustler, Jens Lundell, Jan Kristof Behrens, Ville Kyrki, Matej Hoffmann
We also propose a new simulation environment for this purpose.
no code implementations • 30 Jan 2022 • Andrei Aksjonov, Ville Kyrki
Yet, the latter cannot compete with the first ones in robustness to multiple requirements, for instance, simultaneously addressing safety, comfort, and efficiency.
1 code implementation • 27 Jan 2022 • Rituraj Kaushik, Karol Arndt, Ville Kyrki
In this work, we introduce a novel learning algorithm called SafeAPT that leverages a diverse repertoire of policies evolved in the simulation and transfers the most promising safe policy to the real robot through episodic interaction.
1 code implementation • 20 Jan 2022 • Gabriele Tiboni, Karol Arndt, Ville Kyrki
In recent years, domain randomization over dynamics parameters has gained a lot of traction as a method for sim-to-real transfer of reinforcement learning policies in robotic manipulation; however, finding optimal randomization distributions can be difficult.
1 code implementation • 3 Nov 2021 • Neea Tuomainen, David Blanco-Mulero, Ville Kyrki
In this paper, we propose to use a graph-based representation to model the interaction dynamics of the material and rigid bodies manipulating it.
no code implementations • 14 Sep 2021 • Eshagh Kargar, Ville Kyrki
Driving in a complex urban environment is a difficult task that requires a complex decision policy.
1 code implementation • 2 Sep 2021 • Eshagh Kargar, Ville Kyrki
We propose two novel ways of integrating information across agents and time in MACRPO: First, we use a recurrent layer in critic's network architecture and propose a new framework to use a meta-trajectory to train the recurrent layer.
1 code implementation • 29 Jun 2021 • David Blanco-Mulero, Markus Heinonen, Ville Kyrki
Graph Gaussian Processes (GGPs) provide a data-efficient solution on graph structured domains.
no code implementations • 25 May 2021 • Anton Mallasto, Karol Arndt, Markus Heinonen, Samuel Kaski, Ville Kyrki
In this paper, we present affine transport -- a variant of optimal transport, which models the mapping between state transition distributions between the source and target domains with an affine transformation.
no code implementations • 26 Mar 2021 • Eshagh Kargar, Ville Kyrki
To do this, we train an encoder-decoder deep neural network to predict multiple application-relevant factors such as the trajectories of other agents and the ego car.
no code implementations • 12 Mar 2021 • Karol Arndt, Oliver Struckmeier, Ville Kyrki
Domain adaptation is a common problem in robotics, with applications such as transferring policies from simulation to real world and lifelong learning.
no code implementations • 8 Mar 2021 • Jens Lundell, Francesco Verdoja, Ville Kyrki
Multi-finger grasping in cluttered scenes, on the other hand, remains mostly unexplored due to the added difficulty of reasoning over obstacles which greatly increases the computational time to generate high-quality collision-free grasps.
1 code implementation • 17 Dec 2020 • Jens Lundell, Enric Corona, Tran Nguyen Le, Francesco Verdoja, Philippe Weinzaepfel, Gregory Rogez, Francesc Moreno-Noguer, Ville Kyrki
While there exists many methods for manipulating rigid objects with parallel-jaw grippers, grasping with multi-finger robotic hands remains a quite unexplored research topic.
no code implementations • 11 Dec 2020 • Oliver Struckmeier, Kshitij Tiwari, Ville Kyrki
We find that slow representations lead to equal or better downstream task performance and data efficiency in different experiment domains when compared to representations without slowness regularization.
no code implementations • 16 Oct 2020 • Karol Arndt, Ali Ghadirzadeh, Murtaza Hazara, Ville Kyrki
Few-shot adaptation is a challenging problem in the context of simulation-to-real transfer in robotics, requiring safe and informative data collection.
no code implementations • 16 Oct 2020 • Tran Nguyen Le, Francesco Verdoja, Fares J. Abu-Dakka, Ville Kyrki
Accurately modeling local surface properties of objects is crucial to many robotic applications, from grasping to material recognition.
1 code implementation • 6 Aug 2020 • Francesco Verdoja, Ville Kyrki
Among the various options to estimate uncertainty in deep neural networks, Monte-Carlo dropout is widely popular for its simplicity and effectiveness.
no code implementations • 26 Jul 2020 • Ali Ghadirzadeh, Petra Poklukar, Ville Kyrki, Danica Kragic, Mårten Björkman
We present a data-efficient framework for solving visuomotor sequential decision-making problems which exploits the combination of reinforcement learning (RL) and latent variable generative models.
1 code implementation • 6 Mar 2020 • Kshitij Tiwari, Ville Kyrki, Allen Cheung, Naohide Yamamoto
With the advent of consumer-grade products for presenting an immersive virtual environment (VE), there is a growing interest in utilizing VEs for testing human navigation behavior.
no code implementations • 2 Mar 2020 • Eshagh Kargar, Ville Kyrki
Driving in the dynamic, multi-agent, and complex urban environment is a difficult task requiring a complex decision policy.
Robotics
no code implementations • 16 Sep 2019 • Karol Arndt, Murtaza Hazara, Ali Ghadirzadeh, Ville Kyrki
Modern reinforcement learning methods suffer from low sample efficiency and unsafe exploration, making it infeasible to train robotic policies entirely on real hardware.
no code implementations • 16 Sep 2019 • Oliver Struckmeier, Kshitij Tiwari, Shirin Dora, Martin J. Pearson, Sander M. Bohte, Cyriel MA Pennartz, Ville Kyrki
Extracting and binding salient information from different sensory modalities to determine common features in the environment is a significant challenge in robotics.
no code implementations • 15 Sep 2019 • Jens Lundell, Francesco Verdoja, Ville Kyrki
Current end-to-end grasp planning methods propose grasps in the order of seconds that attain high grasp success rates on a diverse set of objects, but often by constraining the workspace to top-grasps.
no code implementations • 14 Jun 2019 • Oliver Struckmeier, Kshitij Tiwari, Mohammed Salman, Martin J. Pearson, Ville Kyrki
RatSLAM is a rat hippocampus-inspired visual Simultaneous Localization and Mapping (SLAM) framework capable of generating semi-metric topological representations of indoor and outdoor environments.
Robotics
no code implementations • 6 Jun 2019 • Antti Hietanen, Jyrki Latokartano, Alessandro Foi, Roel Pieters, Ville Kyrki, Minna Lanz, Joni-Kristian Kämäräinen
The evaluation metric is based on non-parametric probability density that is estimated from samples of a real physical setup.
1 code implementation • 2 May 2019 • Janne Karttunen, Anssi Kanervisto, Ville Kyrki, Ville Hautamäki
Deep reinforcement learning has proven to be successful for learning tasks in simulated environments, but applying same techniques for robots in real-world domain is more challenging, as they require hours of training.
2 code implementations • 10 Mar 2019 • Aleksi Hämäläinen, Karol Arndt, Ali Ghadirzadeh, Ville Kyrki
Training end-to-end deep robot policies requires a lot of domain-, task-, and hardware-specific data, which is often costly to provide.
2 code implementations • 2 Mar 2019 • Jens Lundell, Francesco Verdoja, Ville Kyrki
We present a method for planning robust grasps over uncertain shape completed objects.
Robotics
1 code implementation • 13 Sep 2018 • Francesco Verdoja, Jens Lundell, Ville Kyrki
Most mobile robots for indoor use rely on 2D laser scanners for localization, mapping and navigation.
1 code implementation • 31 May 2018 • Jens Lundell, Francesco Verdoja, Ville Kyrki
However, those sensors are unable to correctly provide distance to obstacles such as glass panels and tables whose actual occupancy is invisible at the height the sensor is measuring.