161 citations · 1k across the 77 of their papers we have counts for
65 papers · 1 filter
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…
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…
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…
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…
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…
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…
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…
A Novel Simulation-Based Quality Metric for Evaluating Grasps on 3D Deformable Objects
Tran Nguyen Le, Jens Lundell, Fares J. Abu-Dakka +1
Evaluation of grasps on deformable 3D objects is a little-studied problem, even if the applicability of rigid object grasp quality measures for deformable ones is an open question.…
Active Visuo-Haptic Object Shape Completion
Lukas Rustler, Jens Lundell, Jan Kristof Behrens +2
Recent advancements in object shape completion have enabled impressive object reconstructions using only visual input. However, due to self-occlusion, the reconstructions have high…
A Unified Formulation of Geometry-aware Dynamic Movement Primitives
Fares J. Abu-Dakka, Matteo Saveriano, Ville Kyrki
Learning from demonstration (LfD) is considered as an efficient way to transfer skills from humans to robots. Traditionally, LfD has been used to transfer Cartesian and joint posit…
SafeAPT: Safe Simulation-to-Real Robot Learning using Diverse Policies Learned in Simulation
Rituraj Kaushik, Karol Arndt, Ville Kyrki
The framework of Simulation-to-real learning, i.e, learning policies in simulation and transferring those policies to the real world is one of the most promising approaches towards…
DROPO: Sim-to-Real Transfer with Offline Domain Randomization
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 manip…
A Survey of Robot Manipulation in Contact
Markku Suomalainen, Yiannis Karayiannidis, Ville Kyrki
In this survey, we present the current status on robots performing manipulation tasks that require varying contact with the environment, such that the robot must either implicitly…
Manipulation of Granular Materials by Learning Particle Interactions
Neea Tuomainen, David Blanco-Mulero, Ville Kyrki
Manipulation of granular materials such as sand or rice remains an unsolved problem due to challenges such as the difficulty of defining their configuration or modeling the materia…