2.3k citations · 4.7k across the 84 of their papers we have counts for
15 papers · 1 filter
Self-Supervised Learning of State Estimation for Manipulating Deformable Linear Objects
Mengyuan Yan, Yilin Zhu, Ning Jin +1
We demonstrate model-based, visual robot manipulation of linear deformable objects. Our approach is based on a state-space representation of the physical system that the robot aims…
Object-Centric Task and Motion Planning in Dynamic Environments
Toki Migimatsu, Jeannette Bohg
We address the problem of applying Task and Motion Planning (TAMP) in real world environments. TAMP combines symbolic and geometric reasoning to produce sequential manipulation pla…
Accurate Vision-based Manipulation through Contact Reasoning
Alina Kloss, Maria Bauza, Jiajun Wu +3
Planning contact interactions is one of the core challenges of many robotic tasks. Optimizing contact locations while taking dynamics into account is computationally costly and, in…
Learning to Scaffold the Development of Robotic Manipulation Skills
Lin Shao, Toki Migimatsu, Jeannette Bohg
Learning contact-rich, robotic manipulation skills is a challenging problem due to the high-dimensionality of the state and action space as well as uncertainty from noisy sensors a…
Learning from My Partner's Actions: Roles in Decentralized Robot Teams
Dylan P. Losey, Mengxi Li, Jeannette Bohg +1
When teams of robots collaborate to complete a task, communication is often necessary. Like humans, robot teammates should implicitly communicate through their actions: but interpr…
Learning Hierarchical Control for Robust In-Hand Manipulation
Tingguang Li, Krishnan Srinivasan, Max Qing-Hu Meng +2
Robotic in-hand manipulation has been a long-standing challenge due to the complexity of modelling hand and object in contact and of coordinating finger motion for complex manipula…
Learning Visual Dynamics Models of Rigid Objects using Relational Inductive Biases
Fabio Ferreira, Lin Shao, Tamim Asfour +1
Endowing robots with human-like physical reasoning abilities remains challenging. We argue that existing methods often disregard spatio-temporal relations and by using Graph Neural…
Learning Task-Oriented Grasping from Human Activity Datasets
Mia Kokic, Danica Kragic, Jeannette Bohg
We propose to leverage a real-world, human activity RGB dataset to teach a robot Task-Oriented Grasping (TOG). We develop a model that takes as input an RGB image and outputs a han…
UniGrasp: Learning a Unified Model to Grasp with Multifingered Robotic Hands
Lin Shao, Fabio Ferreira, Mikael Jorda +6
To achieve a successful grasp, gripper attributes such as its geometry and kinematics play a role as important as the object geometry. The majority of previous work has focused on…
MeteorNet: Deep Learning on Dynamic 3D Point Cloud Sequences
Xingyu Liu, Mengyuan Yan, Jeannette Bohg
Understanding dynamic 3D environment is crucial for robotic agents and many other applications. We propose a novel neural network architecture called for learning repre…
Learning an Action-Conditional Model for Haptic Texture Generation
Negin Heravi, Wenzhen Yuan, Allison M. Okamura +1
Rich haptic sensory feedback in response to user interactions is desirable for an effective, immersive virtual reality or teleoperation system. However, this feedback depends on ma…
Variable Impedance Control in End-Effector Space: An Action Space for Reinforcement Learning in Contact-Rich Tasks
Roberto Martín-Martín, Michelle A. Lee, Rachel Gardner +3
Reinforcement Learning (RL) of contact-rich manipulation tasks has yielded impressive results in recent years. While many studies in RL focus on varying the observation space or re…
Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks
Michelle A. Lee, Yuke Zhu, Peter Zachares +6
Contact-rich manipulation tasks in unstructured environments often require both haptic and visual feedback. It is non-trivial to manually design a robot controller that combines th…
Learning Manipulation under Physics Constraints with Visual Perception
Wenbin Li, Aleš Leonardis, Jeannette Bohg +1
Understanding physical phenomena is a key competence that enables humans and animals to act and interact under uncertain perception in previously unseen environments containing nov…
Learning to Estimate Pose and Shape of Hand-Held Objects from RGB Images
Mia Kokic, Danica Kragic, Jeannette Bohg
We develop a system for modeling hand-object interactions in 3D from RGB images that show a hand which is holding a novel object from a known category. We design a Convolutional Ne…