16 citations · 42 across the 19 of their papers we have counts for
19 papers
Embedded Inter-Subject Variability in Adversarial Learning for Inertial Sensor-Based Human Activity Recognition
Francisco M. Calatrava-Nicolás, Shoko Miyauchi, Vitor Fortes Rey +3
This paper addresses the problem of Human Activity Recognition (HAR) using data from wearable inertial sensors. An important challenge in HAR is the model's generalization capabili…
Single-View Shape Completion for Robotic Grasping in Clutter
Abhishek Kashyap, Yuxuan Yang, Henrik Andreasson +1
In vision-based robot manipulation, a single camera view can only capture one side of objects of interest, with additional occlusions in cluttered scenes further restricting visibi…
Can Context Bridge the Reality Gap? Sim-to-Real Transfer of Context-Aware Policies
Marco Iannotta, Yuxuan Yang, Johannes A. Stork +2
Sim-to-real transfer remains a major challenge in reinforcement learning (RL) for robotics, as policies trained in simulation often fail to generalize to the real world due to disc…
Exploiting Radiance Fields for Grasp Generation on Novel Synthetic Views
Abhishek Kashyap, Henrik Andreasson, Todor Stoyanov
Vision based robot manipulation uses cameras to capture one or more images of a scene containing the objects to be manipulated. Taking multiple images can help if any object is occ…
Beyond Predefined Actions: Integrating Behavior Trees and Dynamic Movement Primitives for Robot Learning from Demonstration
David Cáceres Domínguez, Erik Schaffernicht, Todor Stoyanov
Interpretable policy representations like Behavior Trees (BTs) and Dynamic Motion Primitives (DMPs) enable robot skill transfer from human demonstrations, but each faces limitation…
On the Fly Adaptation of Behavior Tree-Based Policies through Reinforcement Learning
Marco Iannotta, Johannes A. Stork, Erik Schaffernicht +1
With the rising demand for flexible manufacturing, robots are increasingly expected to operate in dynamic environments where local -- such as slight offsets or size differences in…
KEA: Keeping Exploration Alive by Proactively Coordinating Exploration Strategies
Shih-Min Yang, Martin Magnusson, Johannes A. Stork +1
Soft Actor-Critic (SAC) has achieved notable success in continuous control tasks but struggles in sparse reward settings, where infrequent rewards make efficient exploration challe…
Learning Extrinsic Dexterity with Parameterized Manipulation Primitives
Shih-Min Yang, Martin Magnusson, Johannes A. Stork +1
Many practically relevant robot grasping problems feature a target object for which all grasps are occluded, e.g., by the environment. Single-shot grasp planning invariably fails i…
Heterogeneous Full-body Control of a Mobile Manipulator with Behavior Trees
Marco Iannotta, David Cáceres Domínguez, Johannes A. Stork +2
Integrating the heterogeneous controllers of a complex mechanical system, such as a mobile manipulator, within the same structure and in a modular way is still challenging. In this…
Towards Task-Prioritized Policy Composition
Finn Rietz, Erik Schaffernicht, Todor Stoyanov +1
Combining learned policies in a prioritized, ordered manner is desirable because it allows for modular design and facilitates data reuse through knowledge transfer. In control theo…
Transferring Knowledge for Reinforcement Learning in Contact-Rich Manipulation
Quantao Yang, Johannes A. Stork, Todor Stoyanov
In manufacturing, assembly tasks have been a challenge for learning algorithms due to variant dynamics of different environments. Reinforcement learning (RL) is a promising framewo…
A Stack-of-Tasks Approach Combined with Behavior Trees: a New Framework for Robot Control
David Cáceres Domínguez, Marco Iannotta, Johannes A. Stork +2
Stack-of-Tasks (SoT) control allows a robot to simultaneously fulfill a number of prioritized goals formulated in terms of (in)equality constraints in error space. Since this appro…
Software Architectures for Mobile Robots
Henrik Andreasson, Giorgio Grisetti, Todor Stoyanov +1
A software architecture defines the blueprints of a large computational system, and is thus a crucial part of the design and development effort. This task has been explored extensi…
Sensors for Mobile Robots
Henrik Andreasson, Giorgio Grisetti, Todor Stoyanov +1
A sensor is a device that converts a physical parameter or an environmental characteristic (e.g., temperature, distance, speed, etc.) into a signal that can be digitally measured a…
Ensemble of Sparse Gaussian Process Experts for Implicit Surface Mapping with Streaming Data
Johannes A. Stork, Todor Stoyanov
Creating maps is an essential task in robotics and provides the basis for effective planning and navigation. In this paper, we learn a compact and continuous implicit surface map o…
Object-RPE: Dense 3D Reconstruction and Pose Estimation with Convolutional Neural Networks for Warehouse Robots
Dinh-Cuong Hoang, Todor Stoyanov, Achim J. Lilienthal
We present an approach for recognizing all objects in a scene and estimating their full pose from an accurate 3D instance-aware semantic reconstruction using an RGB-D camera. Our f…
High-quality Instance-aware Semantic 3D Map Using RGB-D Camera
Dinh-Cuong Hoang, Todor Stoyanov, Achim J. Lilienthal
We present a mapping system capable of constructing detailed instance-level semantic models of room-sized indoor environments by means of an RGB-D camera. In this work, we integrat…
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…
An Eigenshapes Approach to Compressed Signed Distance Fields and Their Utility in Robot Mapping
Daniel R. Canelhas, Erik Schaffernicht, Todor Stoyanov +2
In order to deal with the scaling problem of volumetric map representations we propose spatially local methods for high-ratio compression of 3D maps, represented as truncated signe…