161 citations · 1k across the 77 of their papers we have counts for
15 papers · 1 filter
Meta Reinforcement Learning for Sim-to-real Domain Adaptation
Karol Arndt, Murtaza Hazara, Ali Ghadirzadeh +1
Modern reinforcement learning methods suffer from low sample efficiency and unsafe exploration, making it infeasible to train robotic policies entirely on real hardware. In this wo…
MuPNet: Multi-modal Predictive Coding Network for Place Recognition by Unsupervised Learning of Joint Visuo-Tactile Latent Representations
Oliver Struckmeier, Kshitij Tiwari, Shirin Dora +4
Extracting and binding salient information from different sensory modalities to determine common features in the environment is a significant challenge in robotics. Here we present…
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…
Hypermap Mapping Framework and its Application to Autonomous Semantic Exploration
Tobias Zaenker, Francesco Verdoja, Ville Kyrki
Modern intelligent and autonomous robotic applications often require robots to have more information about their environment than that provided by traditional occupancy grid maps.…
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…
ORangE: Operational Range Estimation for Mobile Robot Exploration on a Single Discharge Cycle
Kshitij Tiwari, Xuesu Xiao, Ville Kyrki +1
This paper presents an approach for estimating the operational range for mobile robot exploration on a single battery discharge. Deploying robots in the wild usually requires unint…
ViTa-SLAM: A Bio-inspired Visuo-Tactile SLAM for Navigation while Interacting with Aliased Environments
Oliver Struckmeier, Kshitij Tiwari, Mohammed Salman +2
RatSLAM is a rat hippocampus-inspired visual Simultaneous Localization and Mapping (SLAM) framework capable of generating semi-metric topological representations of indoor and outd…
Object Pose Estimation in Robotics Revisited
Antti Hietanen, Jyrki Latokartano, Alessandro Foi +4
Vision based object grasping and manipulation in robotics require accurate estimation of object's 6D pose. The 6D pose estimation has received significant attention in computer vis…
ViTa-SLAM: Biologically-Inspired Visuo-Tactile SLAM
Oliver Struckmeier, Kshitij Tiwari, Martin J. Pearson +1
In this work, we propose a novel, bio-inspired multi-sensory SLAM approach called ViTa-SLAM. Compared to other multisensory SLAM variants, this approach allows for a seamless multi…
From Video Game to Real Robot: The Transfer between Action Spaces
Janne Karttunen, Anssi Kanervisto, Ville Kyrki +1
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 challengi…
Feedback-based Fabric Strip Folding
Vladimír Petrík, Ville Kyrki
Accurate manipulation of a deformable body such as a piece of fabric is difficult because of its many degrees of freedom and unobservable properties affecting its dynamics. To alle…
Affordance Learning for End-to-End Visuomotor Robot Control
Aleksi Hämäläinen, Karol Arndt, Ali Ghadirzadeh +1
Training end-to-end deep robot policies requires a lot of domain-, task-, and hardware-specific data, which is often costly to provide. In this work, we propose to tackle this issu…
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…
Static Stability of Robotic Fabric Strip Folding
Vladimír Petrík, Vladimír Smutný, Ville Kyrki
Planning accurate manipulation for deformable objects requires prediction of their state. The prediction is often complicated by a loss of stability that may result in collapse of…
Improving dual-arm assembly by master-slave compliance
Markku Suomalainen, Sylvain Calinon, Emmanuel Pignat +1
In this paper we show how different choices regarding compliance affect a dual-arm assembly task. In addition, we present how the compliance parameters can be learned from a human…