Jeannette Bohg

All Publications

• Development and Evaluation of a Learning-Based Model for Real-Time Haptic Texture Rendering IEEE TRANSACTIONS ON HAPTICS Heravi, N., Culbertson, H., Okamura, A. M., Bohg, J. 2024; 17 (4): 705-716

  Abstract

  Current Virtual Reality (VR) environments lack the haptic signals that humans experience during real-life interactions, such as the sensation of texture during lateral movement on a surface. Adding realistic haptic textures to VR environments requires a model that generalizes to variations of a user's interaction and to the wide variety of existing textures in the world. Current methodologies for haptic texture rendering exist, but they usually develop one model per texture, resulting in low scalability. We present a deep learning-based action-conditional model for haptic texture rendering and evaluate its perceptual performance in rendering realistic texture vibrations through a multi-part human user study. This model is unified over all materials and uses data from a vision-based tactile sensor (GelSight) to render the appropriate surface conditioned on the user's action in real-time. For rendering texture, we use a high-bandwidth vibrotactile transducer attached to a 3D Systems Touch device. The results of our user study shows that our learning-based method creates high-frequency texture renderings with comparable or better quality than state-of-the-art methods without the need to learn a separate model per texture. Furthermore, we show that the method is capable of rendering previously unseen textures using a single GelSight image of their surface.

  View details for DOI 10.1109/TOH.2024.3382258

  View details for Web of Science ID 001381756500002

  View details for PubMedID 38536688

• ShaSTA: Modeling Shape and Spatio-Temporal Affinities for 3D Multi-Object Tracking IEEE ROBOTICS AND AUTOMATION LETTERS Sadjadpour, T., Li, J., Ambrus, R., Bohg, J. 2024; 9 (5): 4273-4280

  View details for DOI 10.1109/LRA.2023.3323124

  View details for Web of Science ID 001192358500009

• Design and Control of Roller Grasper V3 for In-Hand Manipulation IEEE TRANSACTIONS ON ROBOTICS Yuan, S., Shao, L., Feng, Y., Sun, J., Xue, T., Yako, C. L., Bohg, J., Salisbury, J. 2024; 40: 4222-4234

  View details for DOI 10.1109/TRO.2024.3454388

  View details for Web of Science ID 001317680300001

• Text2Motion: from natural language instructions to feasible plans AUTONOMOUS ROBOTS Lin, K., Agia, C., Migimatsu, T., Pavone, M., Bohg, J. 2023; 47 (8): 1345-1365

  View details for DOI 10.1007/s10514-023-10131-7

  View details for Web of Science ID 001281797200002

• TidyBot: personalized robot assistance with large language models AUTONOMOUS ROBOTS Wu, J., Antonova, R., Kan, A., Lepert, M., Zeng, A., Song, S., Bohg, J., Rusinkiewicz, S., Funkhouser, T. 2023

  View details for DOI 10.1007/s10514-023-10139-z

  View details for Web of Science ID 001101530300001

• Deep Learning Approaches to Grasp Synthesis: A Review IEEE TRANSACTIONS ON ROBOTICS Newbury, R., Gu, M., Chumbley, L., Mousavian, A., Eppner, C., Leitner, J., Bohg, J., Morales, A., Asfour, T., Kragic, D., Fox, D., Cosgun, A. 2023

  View details for DOI 10.1109/TRO.2023.3280597

  View details for Web of Science ID 001019463600001

• The OBJECTFOLDER BENCHMARK: Multisensory Learning with <i>Neural</i> and <i>Real</i> Objects Gao, R., Dou, Y., Li, H., Agarwal, T., Bohg, J., Li, Y., Fei-Fei, L., Wu, J., IEEE IEEE COMPUTER SOC. 2023: 17276-17286

  View details for DOI 10.1109/CVPR52729.2023.01657

  View details for Web of Science ID 001062531301056

• In-Hand Manipulation of Unknown Objects with Tactile Sensing for Insertion Pan, C., Lepert, M., Yuan, S., Antonova, R., Bohg, J., IEEE IEEE. 2023: 8765-8771

  View details for DOI 10.1109/IROS55552.2023.10341456

  View details for Web of Science ID 001136907802128

• CARTO: Category and Joint Agnostic Reconstruction of ARTiculated Objects Heppert, N., Irshad, M., Zakharov, S., Liu, K., Ambrus, R., Bohg, J., Valada, A., Kollar, T., IEEE IEEE COMPUTER SOC. 2023: 21201-21210

  View details for DOI 10.1109/CVPR52729.2023.02031

  View details for Web of Science ID 001062531305052

• STAP: Sequencing Task-Agnostic Policies Agia, C., Migimatsu, T., Wu, J., Bohg, J., IEEE IEEE. 2023: 7951-7958

  View details for DOI 10.1109/ICRA48891.2023.10160220

  View details for Web of Science ID 001048371101041

• Visuomotor Control in Multi-Object Scenes Using Object-Aware Representations Heravi, N., Wahid, A., Lynch, C., Florence, P., Armstrong, T., Tompson, J., Sermanet, P., Bohg, J., Dwibedi, D., IEEE IEEE. 2023: 9515-9522

  View details for DOI 10.1109/ICRA48891.2023.10160888

  View details for Web of Science ID 001048371102030

• Learning Tool Morphology for Contact-Rich Manipulation Tasks with Differentiable Simulation Li, M., Antonova, R., Sadigh, D., Bohg, J., IEEE IEEE. 2023: 1859-1865

  View details for DOI 10.1109/ICRA48891.2023.10161453

  View details for Web of Science ID 001036713001096

• KITE: Keypoint-Conditioned Policies for Semantic Manipulation Sundaresan, P., Belkhale, S., Sadigh, D., Bohg, J., Tan, J., Toussaint, M., Darvish, K. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2023

  View details for Web of Science ID 001221201501003

• TidyBot: Personalized Robot Assistance with Large Language Models Wu, J., Antonova, R., Kan, A., Lepert, M., Zeng, A., Song, S., Bohg, J., Rusinkiewicz, S., Funkhouser, T., IEEE IEEE. 2023: 3546-3553

  View details for DOI 10.1109/IROS55552.2023.10341577

  View details for Web of Science ID 001133658802091

• Active Task Randomization: Learning Robust Skills via Unsupervised Generation of Diverse and Feasible Tasks Fang, K., Migimatsu, T., Mandlekar, A., Fei-Fei, L., Bohg, J., IEEE IEEE. 2023: 1924-1931

  View details for DOI 10.1109/IROS55552.2023.10341727

  View details for Web of Science ID 001133658801066

• A Bayesian Treatment of Real-to-Sim for Deformable Object Manipulation IEEE ROBOTICS AND AUTOMATION LETTERS Antonova, R., Yang, J., Sundaresan, P., Fox, D., Ramos, F., Bohg, J. 2022; 7 (3): 5819-5826

  View details for DOI 10.1109/LRA.2022.3157377

  View details for Web of Science ID 000778903600002

• Predicting Hand-Object Interaction for Improved Haptic Feedback in Mixed Reality IEEE ROBOTICS AND AUTOMATION LETTERS Salvato, M., Heravi, N., Okamura, A. M., Bohg, J. 2022; 7 (2): 3851-3857

  View details for DOI 10.1109/LRA.2022.3148458

  View details for Web of Science ID 000756831900029

• Vision-Only Robot Navigation in a Neural Radiance World IEEE ROBOTICS AND AUTOMATION LETTERS Adamkiewicz, M., Chen, T., Caccavale, A., Gardner, R., Culbertson, P., Bohg, J., Schwager, M. 2022; 7 (2): 4606-4613

  View details for DOI 10.1109/LRA.2022.3150497

  View details for Web of Science ID 000766269000016

• OBJECTFOLDER 2.0: A Multisensory Object Dataset for Sim2Real Transfer Gao, R., Si, Z., Chang, Y., Clarke, S., Bohg, J., Li Fei-Fei, Yuan, W., Wu, J., IEEE COMP SOC IEEE COMPUTER SOC. 2022: 10588-10598

  View details for DOI 10.1109/CVPR52688.2022.01034

  View details for Web of Science ID 000870759103065

• DiffCloud: Real-to-Sim from Point Clouds with Differentiable Simulation and Rendering of Deformable Objects Sundaresan, P., Antonova, R., Bohg, J., IEEE IEEE. 2022: 10828-10835

  View details for DOI 10.1109/IROS47612.2022.9981101

  View details for Web of Science ID 000909405302132

• Whisker-Inspired Tactile Sensing for Contact Localization on Robot Manipulators Lin, M. A., Reyes, E., Bohg, J., Cutkosky, M. R., IEEE IEEE. 2022: 7817-7824

  View details for DOI 10.1109/IROS47612.2022.9982122

  View details for Web of Science ID 000909405300071

• Category-Independent Articulated Object Tracking with Factor Graphs Heppert, N., Migimatsu, T., Yi, B., Chen, C., Bohg, J., IEEE IEEE. 2022: 3800-3807

  View details for DOI 10.1109/IROS47612.2022.9982029

  View details for Web of Science ID 000908368202123

• Grounding Predicates through Actions open(drawer) open(drawer) Migimatsu, T., Bohg, J., IEEE IEEE. 2022: 3498-3504

  View details for DOI 10.1109/ICRA46639.2022.9812016

  View details for Web of Science ID 000941265702038

• Symbolic State Estimation with Predicates for Contact-Rich Manipulation Tasks Migimatsu, T., Lian, W., Bohg, J., Schaal, S., IEEE IEEE. 2022: 1702-1709

  View details for DOI 10.1109/ICRA46639.2022.9811675

  View details for Web of Science ID 000941265701027

• Dynamic multi-robot task allocation under uncertainty and temporal constraints AUTONOMOUS ROBOTS Choudhury, S., Gupta, J. K., Kochenderfer, M. J., Sadigh, D., Bohg, J. 2021

  View details for DOI 10.1007/s10514-021-10022-9

  View details for Web of Science ID 000714998700002

• Concept2Robot: Learning manipulation concepts from instructions and human demonstrations INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH Shao, L., Migimatsu, T., Zhang, Q., Yang, K., Bohg, J. 2021

  View details for DOI 10.1177/02783649211046285

  View details for Web of Science ID 000709456500001

• Learning latent actions to control assistive robots AUTONOMOUS ROBOTS Losey, D. P., Jeon, H., Li, M., Srinivasan, K., Mandlekar, A., Garg, A., Bohg, J., Sadigh, D. 2021: 1-33

  Abstract

  Assistive robot arms enable people with disabilities to conduct everyday tasks on their own. These arms are dexterous and high-dimensional; however, the interfaces people must use to control their robots are low-dimensional. Consider teleoperating a 7-DoF robot arm with a 2-DoF joystick. The robot is helping you eat dinner, and currently you want to cut a piece of tofu. Today's robots assume a pre-defined mapping between joystick inputs and robot actions: in one mode the joystick controls the robot's motion in the x-y plane, in another mode the joystick controls the robot's z-yaw motion, and so on. But this mapping misses out on the task you are trying to perform! Ideally, one joystick axis should control how the robot stabs the tofu, and the other axis should control different cutting motions. Our insight is that we can achieve intuitive, user-friendly control of assistive robots by embedding the robot's high-dimensional actions into low-dimensional and human-controllable latent actions. We divide this process into three parts. First, we explore models for learning latent actions from offline task demonstrations, and formalize the properties that latent actions should satisfy. Next, we combine learned latent actions with autonomous robot assistance to help the user reach and maintain their high-level goals. Finally, we learn a personalized alignment model between joystick inputs and latent actions. We evaluate our resulting approach in four user studies where non-disabled participants reach marshmallows, cook apple pie, cut tofu, and assemble dessert. We then test our approach with two disabled adults who leverage assistive devices on a daily basis.

  View details for DOI 10.1007/s10514-021-10005-w

  View details for Web of Science ID 000681168800001

  View details for PubMedID 34366568

  View details for PubMedCentralID PMC8335729

• How to train your differentiable filter AUTONOMOUS ROBOTS Kloss, A., Martius, G., Bohg, J. 2021

  View details for DOI 10.1007/s10514-021-09990-9

  View details for Web of Science ID 000659371400001

• Detect, Reject, Correct: Crossmodal Compensation of Corrupted Sensors Lee, M. A., Tan, M., Zhu, Y., Bohg, J., IEEE IEEE. 2021: 909-916

  View details for DOI 10.1109/ICRA48506.2021.9561847

  View details for Web of Science ID 000765738801022

• Differentiable Factor Graph Optimization for Learning Smoothers Yi, B., Lee, M. A., Kloss, A., Martin-Martin, R., Bohg, J., IEEE IEEE. 2021: 1339-1345

  View details for DOI 10.1109/IROS51168.2021.9636300

  View details for Web of Science ID 000755125501017

• TrajectoTree: Trajectory Optimization Meets Tree Search for Planning Multi-contact Dexterous Manipulation 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Chen, C., Culbertson, P., Lepert, M., Schwager, M., Bohg, J. IEEE. 2021: 8262-8268

  View details for DOI 10.1109/IROS51168.2021.9636346

• Probabilistic 3D Multi-Modal, Multi-Object Tracking for Autonomous Driving Chiu, H., Lie, J., Ambrus, R., Bohg, J., IEEE IEEE. 2021: 14227-14233

  View details for DOI 10.1109/ICRA48506.2021.9561754

  View details for Web of Science ID 000771405405099

• Interpreting Contact Interactions to Overcome Failure in Robot Assembly Tasks Zachares, P. A., Lee, M. A., Lian, W., Bohg, J., IEEE IEEE. 2021: 3410-3417

  View details for DOI 10.1109/ICRA48506.2021.9560825

  View details for Web of Science ID 000765738802107

• OmniHang: Learning to Hang Arbitrary Objects using Contact Point Correspondences and Neural Collision Estimation You, Y., Shao, L., Migimatsu, T., Bohg, J., IEEE IEEE. 2021: 5921-5927

  View details for DOI 10.1109/ICRA48506.2021.9560971

  View details for Web of Science ID 000765738804066

• Combining learned and analytical models for predicting action effects from sensory data INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH Kloss, A., Schaal, S., Bohg, J. 2020

  View details for DOI 10.1177/0278364920954896

  View details for Web of Science ID 000569523800001

• Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks IEEE TRANSACTIONS ON ROBOTICS Lee, M. A., Zhu, Y., Zachares, P., Tan, M., Srinivasan, K., Savarese, S., Fei-Fei, L., Garg, A., Bohg, J. 2020; 36 (3): 582–96

  View details for DOI 10.1109/TRO.2019.2959445

  View details for Web of Science ID 000543027200001

• Learning Task-Oriented Grasping From Human Activity Datasets IEEE ROBOTICS AND AUTOMATION LETTERS Kokic, M., Kragic, D., Bohg, J. 2020; 5 (2): 3352–59

  View details for DOI 10.1109/LRA.2020.2975706

  View details for Web of Science ID 000520954200016

• Self-Supervised Learning of State Estimation for Manipulating Deformable Linear Objects IEEE ROBOTICS AND AUTOMATION LETTERS Yan, M., Zhu, Y., Jin, N., Bohg, J. 2020; 5 (2): 2372–79

  View details for DOI 10.1109/LRA.2020.2969931

  View details for Web of Science ID 000526572000036

• Object-Centric Task and Motion Planning in Dynamic Environments IEEE ROBOTICS AND AUTOMATION LETTERS Migimatsu, T., Bohg, J. 2020; 5 (2): 844–51

  View details for DOI 10.1109/LRA.2020.2965875

  View details for Web of Science ID 000510751900008

• UniGrasp: Learning a Unified Model to Grasp With Multifingered Robotic Hands IEEE ROBOTICS AND AUTOMATION LETTERS Shao, L., Ferreira, F., Jorda, M., Nambiar, V., Luo, J., Solowjow, E., Ojea, J., Khatib, O., Bohg, J. 2020; 5 (2): 2286–93

  View details for DOI 10.1109/LRA.2020.2969946

  View details for Web of Science ID 000526572000025

• Concept2Robot: Learning Manipulation Concepts from Instructions and Human Demonstrations Shao, L., Migimatsu, T., Zhang, Q., Yang, K., Bohg, J., Toussaint, M., Bicchi, A., Hermans, T. MIT PRESS. 2020

  View details for Web of Science ID 000570976900082

• Accurate Vision-based Manipulation through Contact Reasoning Kloss, A., Bauza, M., Wu, J., Tenenbaum, J. B., Rodriguez, A., Bohg, J., IEEE IEEE. 2020: 6738-6744

  View details for Web of Science ID 000712319504073

• Learning Hierarchical Control for Robust In-Hand Manipulation Li, T., Srinivasan, K., Meng, M., Yuan, W., Bohg, J., IEEE IEEE. 2020: 8855-8862

  View details for Web of Science ID 000712319505116

• Learning to Scaffold the Development of Robotic Manipulation Skills Shao, L., Migimatsu, T., Bohg, J., IEEE IEEE. 2020: 5671-5677

  View details for Web of Science ID 000712319503133

• Learning User-Preferred Mappings for Intuitive Robot Control Li, M., Losey, D. P., Bohg, J., Sadigh, D., IEEE IEEE. 2020: 10960-10967

  View details for DOI 10.1109/IROS45743.2020.9340909

  View details for Web of Science ID 000724145800103

• Learning Topological Motion Primitives for Knot Planning Yan, M., Li, G., Zhu, Y., Bohg, J., IEEE IEEE. 2020: 9457-9464

  View details for DOI 10.1109/IROS45743.2020.9341330

  View details for Web of Science ID 000724145803058

• Multimodal Sensor Fusion with Differentiable Filters Lee, M. A., Yi, B., Martin-Martin, R., Savarese, S., Bohg, J., IEEE IEEE. 2020: 10444-10451

  View details for DOI 10.1109/IROS45743.2020.9341579

  View details for Web of Science ID 000724145800046

• Dynamic Multi-Robot Task Allocation under Uncertainty and Temporal Constraints Choudhury, S., Gupta, J. K., Kochendeefer, M. J., Sadigh, D., Bohg, J., Toussaint, M., Bicchi, A., Hermans, T. MIT PRESS. 2020

  View details for Web of Science ID 000570976900068

• Predicting grasp success in the real world - A study of quality metrics and human assessment ROBOTICS AND AUTONOMOUS SYSTEMS Rubert, C., Kappler, D., Bohg, J., Morales, A. 2019; 121

  View details for DOI 10.1016/j.robot.2019.103274

  View details for Web of Science ID 000491214500017

• Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks Lee, M. A., Zhu, Y., Srinivasan, K., Shah, P., Savarese, S., Li Fei-Fei, Garg, A., Bohg, J., IEEE, Howard, A., Althoefer, K., Arai, F., Arrichiello, F., Caputo, B., Castellanos, J., Hauser, K., Isler, Kim, J., Liu, H., Oh, P., Santos, Scaramuzza, D., Ude, A., Voyles, R., Yamane, K., Okamura, A. IEEE. 2019: 8943–50

  View details for Web of Science ID 000494942306083

• MeteorNet: Deep Learning on Dynamic 3D Point Cloud Sequences Liu, X., Yan, M., Bohg, J., IEEE IEEE. 2019: 9245–54

  View details for DOI 10.1109/ICCV.2019.00934

  View details for Web of Science ID 000548549204037

• Leveraging Contact Forces for Learning to Grasp Merzic, H., Bogdanovic, M., Kappler, D., Righetti, L., Bohg, J., IEEE, Howard, A., Althoefer, K., Arai, F., Arrichiello, F., Caputo, B., Castellanos, J., Hauser, K., Isler, Kim, J., Liu, H., Oh, P., Santos, Scaramuzza, D., Ude, A., Voyles, R., Yamane, K., Okamura, A. IEEE. 2019: 3615–21

  View details for Web of Science ID 000494942302094

• Learning to Estimate Pose and Shape of Hand-Held Objects from RGB Images Kokic, M., Kragic, D., Bohg, J., IEEE IEEE. 2019: 3980–87

  View details for Web of Science ID 000544658403042

• Variable Impedance Control in End-Effector Space: An Action Space for Reinforcement Learning in Contact-Rich Tasks Martin-Martin, R., Lee, M. A., Gardner, R., Savarese, S., Bohg, J., Garg, A., IEEE IEEE. 2019: 1010–17

  View details for Web of Science ID 000544658400108

• Motion-Based Object Segmentation Based on Dense RGB-D Scene Flow IEEE ROBOTICS AND AUTOMATION LETTERS Shao, L., Shah, P., Dwaracherla, V., Bohg, J. 2018; 3 (4): 3797–3804

  View details for DOI 10.1109/LRA.2018.2856525

  View details for Web of Science ID 000441444700016

• Interactive Perception: Leveraging Action in Perception and Perception in Action IEEE TRANSACTIONS ON ROBOTICS Bohg, J., Hausman, K., Sankaran, B., Brock, O., Kragic, D., Schaal, S., Sukhatme, G. S. 2017; 33 (6): 1273–91

  View details for DOI 10.1109/TRO.2017.2721939

  View details for Web of Science ID 000417841500001

• Reports on the 2017 AAAI Spring Symposium Series AI MAGAZINE Bohg, J., Boix, X., Chang, N., Chu, V., Churchill, E. F., Fang, F., Feldman, J., Gonzalez, A. J., Kido, T., Lawless, W. F., Montana, J. L., Ontanon, S., Sinapov, J., Sofge, D., Steels, L., Steenson, M., Takadama, K., Yadav, A. 2017; 38 (4): 99–106

  View details for DOI 10.1609/aimag.v38i3.2754

  View details for Web of Science ID 000419468800015

• Probabilistic Articulated Real-Time Tracking for Robot Manipulation IEEE ROBOTICS AND AUTOMATION LETTERS Cifuentes, C., Issac, J., Wuethrich, M., Schaal, S., Bohg, J. 2017; 2 (2): 577–84

  View details for DOI 10.1109/LRA.2016.2645124

  View details for Web of Science ID 000413736600027

• On the relevance of grasp metrics for predicting grasp success Rubert, C., Kappler, D., Morales, A., Schaal, S., Bohg, J., Bicchi, A., Okamura, A. IEEE. 2017: 265–72

  View details for Web of Science ID 000426978200038

• Big Data on Robotics. Big data Bohg, J., Ciocarlie, M., Civera, J., Kavraki, L. E. 2016; 4 (4): 195-196

  View details for DOI 10.1089/big.2016.29013.rob

  View details for PubMedID 27992266

• Depth-Based Object Tracking Using a Robust Gaussian Filter Issac, J., Wuethrich, M., Cifuentes, C., Bohg, J., Trimpe, S., Schaal, S., Okamura, A., Menciassi, A., Ude, A., Burschka, D., Lee, D., Arrichiello, F., Liu, H., Moon, H., Neira, J., Sycara, K., Yokoi, K., Martinet, P., Oh, P., Valdastri, P., Krovi IEEE. 2016: 608–15

  View details for Web of Science ID 000389516200075

• Optimizing for what matters: The Top Grasp Hypothesis Kappler, D., Schaal, S., Bohg, J., Okamura, A., Menciassi, A., Ude, A., Burschka, D., Lee, D., Arrichiello, F., Liu, H., Moon, H., Neira, J., Sycara, K., Yokoi, K., Martinet, P., Oh, P., Valdastri, P., Krovi IEEE. 2016: 2167–74

  View details for Web of Science ID 000389516201130

• Exemplar-based Prediction of Global Object Shape from Local Shape Similarity Bohg, J., Kappler, D., Schaal, S., Okamura, A., Menciassi, A., Ude, A., Burschka, D., Lee, D., Arrichiello, F., Liu, H., Moon, H., Neira, J., Sycara, K., Yokoi, K., Martinet, P., Oh, P., Valdastri, P., Krovi IEEE. 2016: 3398–3405

  View details for Web of Science ID 000389516202137

• Learning Where to Search Using Visual Attention Kloss, A., Kappler, D., Lensch, H. A., Butz, M. V., Schaal, S., Bohg, J., IEEE IEEE. 2016: 5238–45

  View details for Web of Science ID 000391921705041

• Robust Gaussian Filtering using a Pseudo Measurement Wuethrich, M., Cifuentes, C., Trimpe, S., Meier, F., Bohg, J., Issac, J., Schaal, S., IEEE IEEE. 2016: 3606–13

  View details for Web of Science ID 000388376103108

• Robot Arm Pose Estimation by Pixel-wise Regression of Joint Angles Widmaier, F., Kappler, D., Schaal, S., Bohg, J., Okamura, A., Menciassi, A., Ude, A., Burschka, D., Lee, D., Arrichiello, F., Liu, H., Moon, H., Neira, J., Sycara, K., Yokoi, K., Martinet, P., Oh, P., Valdastri, P., Krovi IEEE. 2016: 616–23

  View details for Web of Science ID 000389516200076

• Automatic LQR Tuning Based on Gaussian Process Global Optimization Marco, A., Hennig, P., Bohg, J., Schaal, S., Trimpe, S., Okamura, A., Menciassi, A., Ude, A., Burschka, D., Lee, D., Arrichiello, F., Liu, H., Moon, H., Neira, J., Sycara, K., Yokoi, K., Martinet, P., Oh, P., Valdastri, P., Krovi IEEE. 2016: 270–77

  View details for Web of Science ID 000389516200035

• Leveraging Big Data for Grasp Planning Kappler, D., Bohg, J., Schaal, S., IEEE IEEE COMPUTER SOC. 2015: 4304–11

  View details for Web of Science ID 000370974904036

• The Coordinate Particle Filter - A novel Particle Filter for High Dimensional Systems Wuethrich, M., Bohg, J., Kappler, D., Pfreundt, C., Schaal, S., IEEE IEEE COMPUTER SOC. 2015: 2454–61

  View details for Web of Science ID 000370974902067

• Data-Driven Grasp Synthesis-A Survey IEEE TRANSACTIONS ON ROBOTICS Bohg, J., Morales, A., Asfour, T., Kragic, D. 2014; 30 (2): 289–309

  View details for DOI 10.1109/TRO.2013.2289018

  View details for Web of Science ID 000334596700001

• Three-dimensional object reconstruction of symmetric objects by fusing visual and tactile sensing INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH Ilonen, J., Bohg, J., Kyrki, V. 2014; 33 (2): 321–41

  View details for DOI 10.1177/0278364913497816

  View details for Web of Science ID 000333552500006

• Robot Arm Pose Estimation through Pixel-Wise Part Classification Bohg, J., Romero, J., Herzog, A., Schaal, S., IEEE IEEE. 2014: 3143–50

  View details for Web of Science ID 000377221103023

• Dual Execution of Optimized Contact Interaction Trajectories Toussaint, M., Ratliff, N., Bohg, J., Righetti, L., Englert, P., Schaal, S., IEEE IEEE. 2014: 47–54

  View details for Web of Science ID 000349834600008

• Fusing Visual and Tactile Sensing for 3-D Object Reconstruction While Grasping Ilonen, J., Bohg, J., Kyrki, V., IEEE IEEE. 2013: 3547–54

  View details for Web of Science ID 000337617303083

• Probabilistic Object Tracking using a Range Camera Wuethrich, M., Pastor, P., Kalakrishnan, M., Bohg, J., Schaal, S., Amato, N. IEEE. 2013: 3195–3202

  View details for Web of Science ID 000331367403039

• Visual servoing on unknown objects MECHATRONICS Gratal, X., Romero, J., Bohg, J., Kragic, D. 2012; 22 (4): 423–35

  View details for DOI 10.1016/j.mechatronics.2011.09.009

  View details for Web of Science ID 000304847300007

• Mind the Gap - Robotic Grasping under Incomplete Observation IEEE International Conference on Robotics and Automation Bohg, J., Johnson-Roberson, M., Leon, B., Felip, J., Gratal, X., Bergstrom, N., Kragic, D., Morales, A. 2011

  View details for DOI 10.1109/ICRA.2011.5980354

• Enhanced Visual Scene Understanding through Human-Robot Dialog Johnson-Roberson, M., Bohg, J., Skantze, G., Gustafson, J., Carlson, R., Rasolzadeh, B., Kragic, D., IEEE IEEE. 2011: 3342–48

  View details for Web of Science ID 000297477503104

• Learning grasping points with shape context ROBOTICS AND AUTONOMOUS SYSTEMS Bohg, J., Kragic, D. 2010; 58 (4): 362–77

  View details for DOI 10.1016/j.robot.2009.10.003

  View details for Web of Science ID 000276666100003

• OpenGRASP: A Toolkit for Robot Grasping Simulation Leon, B., Ulbrich, S., Diankov, R., Puche, G., Przybylski, M., Morales, A., Asfour, T., Moisio, S., Bohg, J., Kuffner, J., Dillmann, R., Ando, N., Balakirsky, S., Hemker, T., Reggiani, M., VonStryk, O. SPRINGER-VERLAG BERLIN. 2010: 109–20

  View details for Web of Science ID 000329156800013

• Strategies for Multi-Modal Scene Exploration Bohg, J., Johnson-Roberson, M., Bjorkman, M., Kragic, D., IEEE IEEE. 2010: 4509–15

  View details for DOI 10.1109/IROS.2010.5652967

  View details for Web of Science ID 000287672003149

• Attention-based Active 3D Point Cloud Segmentation Johnson-Roberson, M., Bohg, J., Bjorkman, M., Kragic, D., IEEE IEEE. 2010: 1165–70

  View details for DOI 10.1109/IROS.2010.5649872

  View details for Web of Science ID 000287672003158

• TOWARDS GRASP-ORIENTED VISUAL PERCEPTION FOR HUMANOID ROBOTS Bohg, J., Barck-Holst, C., Huebner, K., Ralph, M., Rasolzadeh, B., Song, D., Kragic, D. WORLD SCIENTIFIC PUBL CO PTE LTD. 2009: 387–434

  View details for DOI 10.1142/S0219843609001796

  View details for Web of Science ID 000270041900004

• Integration of Visual Cues for Robotic Grasping Bergstrom, N., Bohg, J., Kragic, D., Fritz, M., Schiele, B., Piater, J. H. SPRINGER-VERLAG BERLIN. 2009: 245–54

  View details for Web of Science ID 000274012700025