161 citations · 1k across the 77 of their papers we have counts for
86 papers
Enhancing Visual Domain Robustness in Behaviour Cloning via Saliency-Guided Augmentation
Zheyu Zhuang, Ruiyu Wang, Nils Ingelhag +2
In vision-based behavior cloning (BC), conventional image augmentations such as Random Crop and Color Jitter often fall short under substantial visual domain shifts, including chan…
Combining Bayesian Inference and Reinforcement Learning for Agent Decision Making: A Review
Chengmin Zhou, Ville Kyrki, Pasi Fränti +1
Bayesian inference has many advantages in decision making of agents (e.g. robotics/simulative agent) over a regular data-driven black-box neural network: Data-efficiency, generaliz…
Raising Body Ownership in End-to-End Visuomotor Policy Learning via Robot-Centric Pooling
Zheyu Zhuang, Ville Kyrki, Danica Kragic
We present Robot-centric Pooling (RcP), a novel pooling method designed to enhance end-to-end visuomotor policies by enabling differentiation between the robots and similar entitie…
COSBO: Conservative Offline Simulation-Based Policy Optimization
Eshagh Kargar, Ville Kyrki
Offline reinforcement learning allows training reinforcement learning models on data from live deployments. However, it is limited to choosing the best combination of behaviors pre…
Jointly Learning Cost and Constraints from Demonstrations for Safe Trajectory Generation
Shivam Chaubey, Francesco Verdoja, Ville Kyrki
Learning from Demonstration allows robots to mimic human actions. However, these methods do not model constraints crucial to ensure safety of the learned skill. Moreover, even when…
Interactive Learning of Physical Object Properties Through Robot Manipulation and Database of Object Measurements
Andrej Kruzliak, Jiri Hartvich, Shubhan P. Patni +6
This work presents a framework for automatically extracting physical object properties, such as material composition, mass, volume, and stiffness, through robot manipulation and a…
From Alexnet to Transformers: Measuring the Non-linearity of Deep Neural Networks with Affine Optimal Transport
Quentin Bouniot, Ievgen Redko, Anton Mallasto +6
In the last decade, we have witnessed the introduction of several novel deep neural network (DNN) architectures exhibiting ever-increasing performance across diverse tasks. Explain…
Benchmarking the Sim-to-Real Gap in Cloth Manipulation
David Blanco-Mulero, Oriol Barbany, Gokhan Alcan +3
Realistic physics engines play a crucial role for learning to manipulate deformable objects such as garments in simulation. By doing so, researchers can circumvent challenges such…
Object-Oriented Grid Mapping in Dynamic Environments
Matti Pekkanen, Francesco Verdoja, Ville Kyrki
Grid maps, especially occupancy grid maps, are ubiquitous in many mobile robot applications. To simplify the process of learning the map, grid maps subdivide the world into a grid…
Fading Expert Guidance: Bridging Model-Based and Learning-Based Control for Abortable Autonomous Overtaking
Jinxiong Lu, Gokhan Alcan, Ville Kyrki
Overtaking on two-lane roads is a safety-critical decision-making problem for autonomous vehicles, since oncoming traffic may force the ego vehicle to abort and merge back to its o…
Localization Under Consistent Assumptions Over Dynamics
Matti Pekkanen, Francesco Verdoja, Ville Kyrki
Accurate maps are a prerequisite for virtually all mobile robot tasks. Most state-of-the-art maps assume a static world; therefore, dynamic objects are filtered out of the measurem…
Learning representations that are closed-form Monge mapping optimal with application to domain adaptation
Oliver Struckmeier, Ievgen Redko, Anton Mallasto +3
Optimal transport (OT) is a powerful geometric tool used to compare and align probability measures following the least effort principle. Despite its widespread use in machine learn…
SPONGE: Sequence Planning with Deformable-ON-Rigid Contact Prediction from Geometric Features
Tran Nguyen Le, Fares J. Abu-Dakka, Ville Kyrki
Planning robotic manipulation tasks, especially those that involve interaction between deformable and rigid objects, is challenging due to the complexity in predicting such interac…
QDP: Learning to Sequentially Optimise Quasi-Static and Dynamic Manipulation Primitives for Robotic Cloth Manipulation
David Blanco-Mulero, Gokhan Alcan, Fares J. Abu-Dakka +1
Pre-defined manipulation primitives are widely used for cloth manipulation. However, cloth properties such as its stiffness or density can highly impact the performance of these pr…
Constrained Generative Sampling of 6-DoF Grasps
Jens Lundell, Francesco Verdoja, Tran Nguyen Le +3
Most state-of-the-art data-driven grasp sampling methods propose stable and collision-free grasps uniformly on the target object. For bin-picking, executing any of those reachable…
Towards High-Definition Maps: a Framework Leveraging Semantic Segmentation to Improve NDT Map Compression and Descriptivity
Petri Manninen, Heikki Hyyti, Ville Kyrki +3
High-Definition (HD) maps are needed for robust navigation of autonomous vehicles, limited by the on-board storage capacity. To solve this, we propose a novel framework, Environmen…
Constrained Trajectory Optimization on Matrix Lie Groups via Lie-Algebraic Differential Dynamic Programming
Gokhan Alcan, Fares J. Abu-Dakka, Ville Kyrki
Matrix Lie groups are an important class of manifolds commonly used in control and robotics, and optimizing control policies on these manifolds is a fundamental problem. In this wo…
LSVL: Large-scale season-invariant visual localization for UAVs
Jouko Kinnari, Riccardo Renzulli, Francesco Verdoja +1
Localization of autonomous unmanned aerial vehicles (UAVs) relies heavily on Global Navigation Satellite Systems (GNSS), which are susceptible to interference. Especially in securi…
Co-Imitation: Learning Design and Behaviour by Imitation
Chang Rajani, Karol Arndt, David Blanco-Mulero +2
The co-adaptation of robots has been a long-standing research endeavour with the goal of adapting both body and behaviour of a system for a given task, inspired by the natural evol…
Learning Stable Robotic Skills on Riemannian Manifolds
Matteo Saveriano, Fares J. Abu-Dakka, Ville Kyrki
In this paper, we propose an approach to learn stable dynamical systems evolving on Riemannian manifolds. The approach leverages a data-efficient procedure to learn a diffeomorphic…
Bayesian Floor Field: Transferring people flow predictions across environments
Francesco Verdoja, Tomasz Piotr Kucner, Ville Kyrki
Mapping people dynamics is a crucial skill for robots, because it enables them to coexist in human-inhabited environments. However, learning a model of people dynamics is a time co…
Learning Based High-Level Decision Making for Abortable Overtaking in Autonomous Vehicles
Ehsan Malayjerdi, Gokhan Alcan, Eshagh Kargar +3
Autonomous vehicles are a growing technology that aims to enhance safety, accessibility, efficiency, and convenience through autonomous maneuvers ranging from lane change to overta…
Online vs. Offline Adaptive Domain Randomization Benchmark
Gabriele Tiboni, Karol Arndt, Giuseppe Averta +2
Physics simulators have shown great promise for conveniently learning reinforcement learning policies in safe, unconstrained environments. However, transferring the acquired knowle…
Training and Evaluation of Deep Policies using Reinforcement Learning and Generative Models
Ali Ghadirzadeh, Petra Poklukar, Karol Arndt +4
We present a data-efficient framework for solving sequential decision-making problems which exploits the combination of reinforcement learning (RL) and latent variable generative m…