9 citations · 33 across the 67 of their papers we have counts for
30 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…
On the Generalization Capabilities, Design Choices and Limitations of Keypoint Imitation Learning
Thomas Lips, Marco Moletta, Michael C. Welle +2
RGB-based imitation learning requires many demonstrations to generalize to unseen objects or scenes, motivating research into intermediate representations to improve generalization…
Preference Aligned Visuomotor Diffusion Policies for Deformable Object Manipulation
Marco Moletta, Michael C. Welle, Danica Kragic
Humans naturally develop preferences for how manipulation tasks should be performed, which are often subtle, personal, and difficult to articulate. Although it is important for rob…
Reduced-order Control and Geometric Structure of Learned Lagrangian Latent Dynamics
Katharina Friedl, Noémie Jaquier, Seungyeon Kim +2
Model-based controllers can offer strong guarantees on stability and convergence by relying on physically accurate dynamic models. However, these are rarely available for high-dime…
PALM: Enhanced Generalizability for Local Visuomotor Policies via Perception Alignment
Ruiyu Wang, Zheyu Zhuang, Danica Kragic +1
Generalizing beyond the training domain in image-based behavior cloning remains challenging. Existing methods address individual axes of generalization, workspace shifts, viewpoint…
Hybrid Imitation Learning: Teleoperation Augmentation Primitives that Policies Learn to Trigger
Jonne Van Haastregt, Bastian Orthmann, Michael C. Welle +2
What an operator can demonstrate bounds what imitation learning can learn. Teleoperation interfaces map the human body to the robot, so motions that are hard for a human, such as h…
VBM-NET: Visual Base Pose Learning for Mobile Manipulation using Equivariant TransporterNet and GNNs
Lakshadeep Naik, Adam Fischer, Daniel Duberg +1
In Mobile Manipulation, selecting an optimal mobile base pose is essential for successful object grasping. Previous works have addressed this problem either through classical plann…
Real-Time Iteration Scheme for Diffusion Policy
Yufei Duan, Hang Yin, Danica Kragic
Diffusion Policies have demonstrated impressive performance in robotic manipulation tasks. However, their long inference time, resulting from an extensive iterative denoising proce…
FLoRA: Sample-Efficient Preference-based RL via Low-Rank Style Adaptation of Reward Functions
Daniel Marta, Simon Holk, Miguel Vasco +6
Preference-based reinforcement learning (PbRL) is a suitable approach for style adaptation of pre-trained robotic behavior: adapting the robot's policy to follow human user prefere…
Pushing Everything Everywhere All At Once: Probabilistic Prehensile Pushing
Patrizio Perugini, Jens Lundell, Katharina Friedl +1
We address prehensile pushing, the problem of manipulating a grasped object by pushing against the environment. Our solution is an efficient nonlinear trajectory optimization probl…
Learning Dexterous In-Hand Manipulation with Multifingered Hands via Visuomotor Diffusion
Piotr Koczy, Michael C. Welle, Danica Kragic
We present a framework for learning dexterous in-hand manipulation with multifingered hands using visuomotor diffusion policies. Our system enables complex in-hand manipulation tas…
Grasping a Handful: Sequential Multi-Object Dexterous Grasp Generation
Haofei Lu, Yifei Dong, Zehang Weng +3
We introduce the sequential multi-object robotic grasp sampling algorithm SeqGrasp that can robustly synthesize stable grasps on diverse objects using the robotic hand's partial De…
FLAME: A Federated Learning Benchmark for Robotic Manipulation
Santiago Bou Betran, Alberta Longhini, Miguel Vasco +2
Recent progress in robotic manipulation has been fueled by large-scale datasets collected across diverse environments. Training robotic manipulation policies on these datasets is t…
Early Detection of Human Handover Intentions in Human-Robot Collaboration: Comparing EEG, Gaze, and Hand Motion
Parag Khanna, Nona Rajabi, Sumeyra U. Demir Kanik +3
Human-robot collaboration (HRC) relies on accurate and timely recognition of human intentions to ensure seamless interactions. Among common HRC tasks, human-to-robot object handove…
Real-Time Operator Takeover for Visuomotor Diffusion Policy Training
Marco Moletta, Michael C. Welle, Nils Ingelhag +2
We present a Real-Time Operator Takeover (RTOT) paradigm that enables operators to seamlessly take control of a live visuomotor diffusion policy, guiding the system back to desirab…
The 1st InterAI Workshop: Interactive AI for Human-centered Robotics
Yuchong Zhang, Elmira Yadollahi, Yong Ma +3
The workshop is affiliated with 33nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN 2024) August 26~30, 2023 / Pasadena, CA, USA. It is designed…
Feature Extractor or Decision Maker: Rethinking the Role of Visual Encoders in Visuomotor Policies
Ruiyu Wang, Zheyu Zhuang, Shutong Jin +3
An end-to-end (E2E) visuomotor policy is typically treated as a unified whole, but recent approaches using out-of-domain (OOD) data to pretrain the visual encoder have cleanly sepa…
Puppeteer Your Robot: Augmented Reality Leader-Follower Teleoperation
Jonne van Haastregt, Michael C. Welle, Yuchong Zhang +1
High-quality demonstrations are necessary when learning complex and challenging manipulation tasks. In this work, we introduce an approach to puppeteer a robot by controlling a vir…
Unfolding the Literature: A Review of Robotic Cloth Manipulation
Alberta Longhini, Yufei Wang, Irene Garcia-Camacho +9
The realm of textiles spans clothing, households, healthcare, sports, and industrial applications. The deformable nature of these objects poses unique challenges that prior work on…
Visual Action Planning with Multiple Heterogeneous Agents
Martina Lippi, Michael C. Welle, Marco Moletta +3
Visual planning methods are promising to handle complex settings where extracting the system state is challenging. However, none of the existing works tackles the case of multiple…
Low-Cost Teleoperation with Haptic Feedback through Vision-based Tactile Sensors for Rigid and Soft Object Manipulation
Martina Lippi, Michael C. Welle, Maciej K. Wozniak +2
Haptic feedback is essential for humans to successfully perform complex and delicate manipulation tasks. A recent rise in tactile sensors has enabled robots to leverage the sense o…
A Robotic Skill Learning System Built Upon Diffusion Policies and Foundation Models
Nils Ingelhag, Jesper Munkeby, Jonne van Haastregt +3
In this paper, we build upon two major recent developments in the field, Diffusion Policies for visuomotor manipulation and large pre-trained multimodal foundational models to obta…
Mind Meets Robots: A Review of EEG-Based Brain-Robot Interaction Systems
Yuchong Zhang, Nona Rajabi, Farzaneh Taleb +4
Brain-robot interaction (BRI) empowers individuals to control (semi-)automated machines through their brain activity, either passively or actively. In the past decade, BRI systems…
Standardization of Cloth Objects and its Relevance in Robotic Manipulation
Irene Garcia-Camacho, Alberta Longhini, Michael Welle +3
The field of robotics faces inherent challenges in manipulating deformable objects, particularly in understanding and standardising fabric properties like elasticity, stiffness, an…