4 citations · 7 across the 28 of their papers we have counts for
29 papers
Physics Residual Dynamics and Reduced Order Whole-Body Planning for Obstacle Aware Human Robot Cloth CoTransportation
Moein Forouhar, Kosar Behnia, Anirvan Dutta +5
Human--robot co-transportation of deformable objects requires predicting object deformation during motion, since obstacle clearance depends on both the grasp points and the unactua…
Barrier Certificate Synthesis for Non-Polynomial Robotic Dynamics via Polynomial Lifting
Shivam Chaubey, Francesco Verdoja, Shankar Deka +1
Safe operation of robotic systems requires trajectories to remain within a prescribed safe set under admissible control inputs. Barrier certificates provide such guarantees by cert…
Deformable Object Manipulation under Partial Observability via Real-Time Full-Shape Estimation
Kosar Behnia, Ville Kyrki, Gokhan Alcan
Manipulating deformable objects (DOs) is challenging due to their high-dimensional state space, underactuated dynamics, and partial observability. In this paper, we propose cRVAE,…
Emergency Stopping for Liquid-manipulating Robots
Samuli Hynninen, Ville Kyrki
Manipulating open liquid containers is challenging because liquids are highly sensitive to vessel accelerations and jerks. Although spill-free liquid manipulation has been widely s…
Relational Scene Graphs for Object Grounding of Natural Language Commands
Julia Kuhn, Francesco Verdoja, Tsvetomila Mihaylova +1
Robots are finding wider adoption in human environments, increasing the need for natural human-robot interaction. However, understanding a natural language command requires the rob…
Event-Grounding Graph: Unified Spatio-Temporal Scene Graph from Robotic Observations
Phuoc Nguyen, Francesco Verdoja, Ville Kyrki
A fundamental aspect for building intelligent autonomous robots that can assist humans in their daily lives is the construction of rich environmental representations. While advance…
QuASH: Using Natural-Language Heuristics to Query Visual-Language Robotic Maps
Matti Pekkanen, Francesco Verdoja, Ville Kyrki
Embeddings from Visual-Language Models are increasingly utilized to represent semantics in robotic maps, offering an open-vocabulary scene understanding that surpasses traditional,…
IA-VLA: Input Augmentation for Vision-Language-Action models in settings with semantically complex tasks
Eric Hannus, Miika Malin, Tran Nguyen Le +1
Vision-language-action models (VLAs) have become an increasingly popular approach for addressing robot manipulation problems in recent years. However, such models need to output ac…
Efficient Human-Aware Task Allocation for Multi-Robot Systems in Shared Environments
Maryam Kazemi Eskeri, Ville Kyrki, Dominik Baumann +1
Multi-robot systems are increasingly deployed in applications, such as intralogistics or autonomous delivery, where multiple robots collaborate to complete tasks efficiently. One o…
A Lightweight Crowd Model for Robot Social Navigation
Maryam Kazemi Eskeri, Thomas Wiedemann, Ville Kyrki +2
Robots operating in human-populated environments must navigate safely and efficiently while minimizing social disruption. Achieving this requires estimating crowd movement to avoid…
MoDeSuite: Robot Learning Task Suite for Benchmarking Mobile Manipulation with Deformable Objects
Yuying Zhang, Kevin Sebastian Luck, Francesco Verdoja +2
Mobile manipulation is a critical capability for robots operating in diverse, real-world environments. However, manipulating deformable objects and materials remains a major challe…
Minimal Intervention Shared Control with Guaranteed Safety under Non-Convex Constraints
Shivam Chaubey, Francesco Verdoja, Shankar Deka +1
Shared control combines human intention with autonomous decision-making. At the low level, the primary goal is to maintain safety regardless of the user's input to the system. Howe…
Discrete Contrastive Learning for Diffusion Policies in Autonomous Driving
Kalle Kujanpää, Daulet Baimukashev, Farzeen Munir +4
Learning to perform accurate and rich simulations of human driving behaviors from data for autonomous vehicle testing remains challenging due to human driving styles' high diversit…
Injecting Conflict Situations in Autonomous Driving Simulation using CARLA
Tsvetomila Mihaylova, Stefan Reitmann, Elin A. Topp +1
Simulation of conflict situations for autonomous driving research is crucial for understanding and managing interactions between Automated Vehicles (AVs) and human drivers. This pa…
REACT: Real-time Efficient Attribute Clustering and Transfer for Updatable 3D Scene Graph
Phuoc Nguyen, Francesco Verdoja, Ville Kyrki
Modern-day autonomous robots need high-level map representations to perform sophisticated tasks. Recently, 3D scene graphs (3DSGs) have emerged as a promising alternative to tradit…
Transfer Learning in Latent Contextual Bandits with Covariate Shift Through Causal Transportability
Mingwei Deng, Ville Kyrki, Dominik Baumann
Transferring knowledge from one environment to another is an essential ability of intelligent systems. Nevertheless, when two environments are different, naively transferring all k…
Trajectory Planning and Control for Robotic Magnetic Manipulation
Ogulcan Isitman, Gokhan Alcan, Ville Kyrki
Robotic magnetic manipulation offers a minimally invasive approach to gastrointestinal examinations through capsule endoscopy. However, controlling such systems using external perm…
Data-driven Diffusion Models for Enhancing Safety in Autonomous Vehicle Traffic Simulations
Jinxiong Lu, Shoaib Azam, Gokhan Alcan +1
Safety-critical traffic scenarios are integral to the development and validation of autonomous driving systems. These scenarios provide crucial insights into vehicle responses unde…
Constrained Trajectory Optimization for Hybrid Dynamical Systems
Pietro Noah Crestaz, Gokhan Alcan, Ville Kyrki
Hybrid dynamical systems pose significant challenges for effective planning and control, especially when additional constraints such as obstacle avoidance, state boundaries, and ac…
Learning Transparent Reward Models via Unsupervised Feature Selection
Daulet Baimukashev, Gokhan Alcan, Kevin Sebastian Luck +1
In complex real-world tasks such as robotic manipulation and autonomous driving, collecting expert demonstrations is often more straightforward than specifying precise learning obj…
ReMoBot: Retrieval-Based Few-Shot Imitation Learning for Mobile Manipulation with Vision Foundation Models
Yuying Zhang, Wenyan Yang, Francesco Verdoja +2
Imitation learning (IL) algorithms typically distill demonstrations into parametric policies to mimic expert behavior. However, with limited data and partial observability, such as…
Automated Feature Selection for Inverse Reinforcement Learning
Daulet Baimukashev, Gokhan Alcan, Ville Kyrki
Inverse reinforcement learning (IRL) is an imitation learning approach to learning reward functions from expert demonstrations. Its use avoids the difficult and tedious procedure o…
Dynamic Manipulation of Deformable Objects using Imitation Learning with Adaptation to Hardware Constraints
Eric Hannus, Tran Nguyen Le, David Blanco-Mulero +1
Imitation Learning (IL) is a promising paradigm for learning dynamic manipulation of deformable objects since it does not depend on difficult-to-create accurate simulations of such…
Online Learning of Human Constraints from Feedback in Shared Autonomy
Shibei Zhu, Tran Nguyen Le, Samuel Kaski +1
Real-time collaboration with humans poses challenges due to the different behavior patterns of humans resulting from diverse physical constraints. Existing works typically focus on…