Jeannette Bohg

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Stanford University

Jeannette Bohg is an Assistant Professor of Computer Science at Stanford University. She was a group leader at the Autonomous Motion Department (AMD) of the MPI for Intelligent Systems until September 2017. Before joining AMD in January 2012, Jeannette Bohg was a PhD student at the Computer Vision and Active Perception lab (CVAP) at KTH in Stockholm. In her thesis she proposed novel methods towards multi-modal scene understanding for robotic grasping. She studied at Chalmers in Gothenburg and at the Technical University in Dresden where she received her Master in Art and Technology and her Diploma in Computer Science, respectively. Her research focuses on perception and learning for autonomous robotic manipulation and grasping. She is specifically interesting in developing methods that are goal-directed, real-time and multi-modal such that they can provide meaningful feedback for execution and learning.

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Danica Kragic
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Learning to Scaffold the Development of Robotic Manipulation Skills

Learning contact-rich, robotic manipulation skills is a challenging prob...
11/03/2019 ∙ by Lin Shao, et al. ∙ 29 ∙ share

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UniGrasp: Learning a Unified Model to Grasp with N-Fingered Robotic Hands

To achieve a successful grasp, gripper attributes including geometry and...
10/24/2019 ∙ by Lin Shao, et al. ∙ 25 ∙ share

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MeteorNet: Deep Learning on Dynamic 3D Point Cloud Sequences

Understanding dynamic 3D environment is crucial for robotic agents and m...
10/21/2019 ∙ by Xingyu Liu, et al. ∙ 20 ∙ share

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Accurate Vision-based Manipulation through Contact Reasoning

Planning contact interactions is one of the core challenges of many robo...
11/08/2019 ∙ by Alina Kloss, et al. ∙ 18 ∙ share

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Self-Supervised Learning of State Estimation for Manipulating Deformable Linear Objects

We demonstrate model-based, visual robot manipulation of linear deformab...
11/14/2019 ∙ by Mengyuan Yan, et al. ∙ 18 ∙ share

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Dynamic Multi-Robot Task Allocation under Uncertainty and Temporal Constraints

We consider the problem of dynamically allocating tasks to multiple agen...
05/27/2020 ∙ by Shushman Choudhury, et al. ∙ 17 ∙ share

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Probabilistic 3D Multi-Object Tracking for Autonomous Driving

3D multi-object tracking is a key module in autonomous driving applicati...
01/16/2020 ∙ by Hsu-kuang Chiu, et al. ∙ 14 ∙ share

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Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks

Contact-rich manipulation tasks in unstructured environments often requi...
10/24/2018 ∙ by Michelle A. Lee, et al. ∙ 4 ∙ share

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Variable Impedance Control in End-Effector Space: An Action Space for Reinforcement Learning in Contact-Rich Tasks

Reinforcement Learning (RL) of contact-rich manipulation tasks has yield...
06/20/2019 ∙ by Roberto Martín-Martín, et al. ∙ 2 ∙ share

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Latest Datasets and Technologies Presented in the Workshop on Grasping and Manipulation Datasets

This paper reports the activities and outcomes in the Workshop on Graspi...
09/08/2016 ∙ by Matteo Bianchi, et al. ∙ 0 ∙ share

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Robust Gaussian Filtering using a Pseudo Measurement

Many sensors, such as range, sonar, radar, GPS and visual devices, produ...
09/14/2015 ∙ by Manuel Wüthrich, et al. ∙ 0 ∙ share

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Depth-Based Object Tracking Using a Robust Gaussian Filter

We consider the problem of model-based 3D-tracking of objects given dens...
02/19/2016 ∙ by Jan Issac, et al. ∙ 0 ∙ share

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Combining learned and analytical models for predicting action effects

One of the most basic skills a robot should possess is predicting the ef...
10/11/2017 ∙ by Alina Kloss, et al. ∙ 0 ∙ share

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Acquiring Target Stacking Skills by Goal-Parameterized Deep Reinforcement Learning

Understanding physical phenomena is a key component of human intelligenc...
11/01/2017 ∙ by Wenbin Li, et al. ∙ 0 ∙ share

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Motion-based Object Segmentation based on Dense RGB-D Scene Flow

Given two consecutive RGB-D images, we propose a model that estimates a ...
04/14/2018 ∙ by Lin Shao, et al. ∙ 0 ∙ share

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ClusterNet: Instance Segmentation in RGB-D Images

We propose a method for instance-level segmentation that uses RGB-D data...
07/24/2018 ∙ by Lin Shao, et al. ∙ 0 ∙ share

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Grasp success prediction with quality metrics

Current robotic manipulation requires reliable methods to predict whethe...
09/10/2018 ∙ by Carlos Rubert, et al. ∙ 0 ∙ share

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Leveraging Contact Forces for Learning to Grasp

Grasping objects under uncertainty remains an open problem in robotics r...
09/19/2018 ∙ by Hamza Merzic, et al. ∙ 0 ∙ share

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Learning to Estimate Pose and Shape of Hand-Held Objects from RGB Images

We develop a system for modeling hand-object interactions in 3D from RGB...
03/08/2019 ∙ by Mia Kokic, et al. ∙ 0 ∙ share

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Learning Manipulation under Physics Constraints with Visual Perception

Understanding physical phenomena is a key competence that enables humans...
04/19/2019 ∙ by Wenbin Li, et al. ∙ 0 ∙ share

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Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks

Contact-rich manipulation tasks in unstructured environments often requi...
07/28/2019 ∙ by Michelle A. Lee, et al. ∙ 0 ∙ share

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Learning Visual Dynamics Models of Rigid Objects using Relational Inductive Biases

Endowing robots with human-like physical reasoning abilities remains cha...
09/09/2019 ∙ by Fabio Ferreira, et al. ∙ 0 ∙ share

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Learning an Action-Conditional Model for Haptic Texture Generation

Rich haptic sensory feedback in response to user interactions is desirab...
09/28/2019 ∙ by Negin Heravi, et al. ∙ 0 ∙ share

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Learning from My Partner's Actions: Roles in Decentralized Robot Teams

When teams of robots collaborate to complete a task, communication is of...
10/16/2019 ∙ by Dylan P. Losey, et al. ∙ 0 ∙ share

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Learning Task-Oriented Grasping from Human Activity Datasets

We propose to leverage a real-world, human activity RGB datasets to teac...
10/25/2019 ∙ by Mia Kokic, et al. ∙ 0 ∙ share

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Learning Hierarchical Control for Robust In-Hand Manipulation

Robotic in-hand manipulation has been a long-standing challenge due to t...
10/24/2019 ∙ by Tingguang Li, et al. ∙ 0 ∙ share

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Object-Centric Task and Motion Planning in Dynamic Environments

We address the problem of applying Task and Motion Planning (TAMP) in re...
11/12/2019 ∙ by Toki Migimatsu, et al. ∙ 0 ∙ share

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