59 citations · 219 across the 16 of their papers we have counts for
17 papers
Enabling Robot Manipulation of Soft and Rigid Objects with Vision-based Tactile Sensors
Michael C. Welle, Martina Lippi, Haofei Lu +3
Endowing robots with tactile capabilities opens up new possibilities for their interaction with the environment, including the ability to handle fragile and/or soft objects. In thi…
GoNet: An Approach-Constrained Generative Grasp Sampling Network
Zehang Weng, Haofei Lu, Jens Lundell +1
This work addresses the problem of learning approach-constrained data-driven grasp samplers. To this end, we propose GoNet: a generative grasp sampler that can constrain the grasp…
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…
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…
Deformation-Aware Data-Driven Grasp Synthesis
Tran Nguyen Le, Jens Lundell, Fares J. Abu-Dakka +1
Grasp synthesis for 3D deformable objects remains a little-explored topic, most works aiming to minimize deformations. However, deformations are not necessarily harmful -- humans a…
Towards synthesizing grasps for 3D deformable objects with physics-based simulation
Tran Nguyen Le, Jens Lundell, Fares J. Abu-Dakka +1
Grasping deformable objects is not well researched due to the complexity in modelling and simulating the dynamic behavior of such objects. However, with the rapid development of ph…
DDGC: Generative Deep Dexterous Grasping in Clutter
Jens Lundell, Francesco Verdoja, Ville Kyrki
Recent advances in multi-fingered robotic grasping have enabled fast 6-Degrees-Of-Freedom (DOF) single object grasping. Multi-finger grasping in cluttered scenes, on the other hand…
Multi-FinGAN: Generative Coarse-To-Fine Sampling of Multi-Finger Grasps
Jens Lundell, Enric Corona, Tran Nguyen Le +5
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. Reaso…
POMDP Manipulation Planning under Object Composition Uncertainty
Joni Pajarinen, Jens Lundell, Ville Kyrki
Manipulating unknown objects in a cluttered environment is difficult because segmentation of the scene into objects, that is, object composition is uncertain. Due to this uncertain…
Beyond Top-Grasps Through Scene Completion
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 wor…
Safe Grasping with a Force Controlled Soft Robotic Hand
Tran Nguyen Le, Jens Lundell, Ville Kyrki
Safe yet stable grasping requires a robotic hand to apply sufficient force on the object to immobilize it while keeping it from getting damaged. Soft robotic hands have been propos…
Robust Grasp Planning Over Uncertain Shape Completions
Jens Lundell, Francesco Verdoja, Ville Kyrki
We present a method for planning robust grasps over uncertain shape completed objects. For shape completion, a deep neural network is trained to take a partial view of the object a…
Safe-To-Explore State Spaces: Ensuring Safe Exploration in Policy Search with Hierarchical Task Optimization
Jens Lundell, Robert Krug, Erik Schaffernicht +2
Policy search reinforcement learning allows robots to acquire skills by themselves. However, the learning procedure is inherently unsafe as the robot has no a-priori way to predict…
Deep Network Uncertainty Maps for Indoor Navigation
Francesco Verdoja, Jens Lundell, Ville Kyrki
Most mobile robots for indoor use rely on 2D laser scanners for localization, mapping and navigation. These sensors, however, cannot detect transparent surfaces or measure the full…
Imitating Human Search Strategies for Assembly
Dennis Ehlers, Markku Suomalainen, Jens Lundell +1
We present a Learning from Demonstration method for teaching robots to perform search strategies imitated from humans in scenarios where alignment tasks fail due to position uncert…
Hallucinating robots: Inferring Obstacle Distances from Partial Laser Measurements
Jens Lundell, Francesco Verdoja, Ville Kyrki
Many mobile robots rely on 2D laser scanners for localization, mapping, and navigation. However, those sensors are unable to correctly provide distance to obstacles such as glass p…