Fetching the paper…
Reading the bibliography…
Embodied Artificial Intelligence (Embodied AI) is crucial for achieving Artificial General Intelligence (AGI) and serves as a foundation for various applications (e.g., intelligent mechatronics systems, smart manufacturing) that bridge cyberspace and the physical world.
X. Chen, H. Ma, J. Wan, B. Li, and T. Xia, “Multi-view 3d object detection network for autonomous driving,” in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition , 2017, pp. 1907–1915
1915
Earlier work this paper cites.
N. C. Metropolis and S. M. Ulam, “The monte carlo method.” Journal of the American Statistical Association , vol. 44 247, pp. 335–41, 1949
1949
Earlier work this paper cites.
C. Machinery, “Computing machinery and intelligence-am turing,” Mind , vol. 59, no. 236, p. 433, 1950
1950
Earlier work this paper cites.
P. E. Hart, N. J. Nilsson, and B. Raphael, “A formal basis for the heuristic determination of minimum cost paths,” IEEE Trans. Syst. Sci. Cybern. , vol. 4, pp. 100–107, 1968
1968
Earlier work this paper cites.
R. E. Fikes and N. J. Nilsson, “Strips: A new approach to the application of theorem proving to problem solving,” Artificial Intelligence , vol. 2, no. 3, pp. 189–208, 1971
1971
Earlier work this paper cites.
H. Miura and I. Shimoyama, “Dynamic walk of a biped,” The International Journal of Robotics Research , vol. 3, no. 2, pp. 60–74, 1984
1984
Earlier work this paper cites.
S. J. Lederman and R. L. Klatzky, “Hand movements: A window into haptic object recognition,” Cognitive psychology , vol. 19, no. 3, pp. 342–368, 1987
1987
Earlier work this paper cites.
J. Haugeland, Artificial intelligence: The very idea . MIT press, 1989
1989
Earlier work this paper cites.
B. Yamauchi, “A frontier-based approach for autonomous exploration,” in Proceedings IEEE International Symposium on Computational Intelligence in Robotics and Automation CIRA’97.’Towards New Computational Principles for Robotics and Automation’ , 1997, pp. 146–151
1997
Earlier work this paper cites.
D. McDermott, M. Ghallab, A. E. Howe, C. A. Knoblock, A. Ram, M. M. Veloso, D. S. Weld, and D. E. Wilkins, “Pddl-the planning domain definition language,” 1998
1998
Earlier work this paper cites.
R. Pfeifer and F. Iida, “Embodied artificial intelligence: Trends and challenges,” Lecture notes in computer science , pp. 1–26, 2004
2004
Earlier work this paper cites.
B. M. Yamauchi, “Packbot: a versatile platform for military robotics,” in Unmanned ground vehicle technology VI , vol. 5422. SPIE, 2004, pp. 228–237
2004
Earlier work this paper cites.
N. Koenig and A. Howard, “Design and use paradigms for gazebo, an open-source multi-robot simulator,” in IEEE/RSJ International Conference on Intelligent Robots and Systems , vol. 3, 2004, pp. 2149–2154
2004
Earlier work this paper cites.
N. Koenig and A. Howard, “Design and use paradigms for gazebo, an open-source multi-robot simulator,” in IEEE/RSJ International Conference on Intelligent Robots and Systems , vol. 3, 2004, pp. 2149–2154
2004
Earlier work this paper cites.
R. Pfeifer and J. Bongard, How the body shapes the way we think: a new view of intelligence . MIT press, 2006
2006
Earlier work this paper cites.
H. Durrant-Whyte and T. Bailey, “Simultaneous localization and mapping: part i,” IEEE robotics & automation magazine , vol. 13, no. 2, pp. 99–110, 2006
2006
Earlier work this paper cites.
T. Bailey and H. Durrant-Whyte, “Simultaneous localization and mapping (slam): Part ii,” IEEE robotics & automation magazine , vol. 13, no. 3, pp. 108–117, 2006
2006
Earlier work this paper cites.
A. J. Davison, I. D. Reid, N. D. Molton, and O. Stasse, “Monoslam: Real-time single camera slam,” IEEE transactions on pattern analysis and machine intelligence , vol. 29, no. 6, pp. 1052–1067, 2007
2007
Earlier work this paper cites.
A. I. Mourikis and S. I. Roumeliotis, “A multi-state constraint kalman filter for vision-aided inertial navigation,” in IEEE International Conference on Robotics and Automation . IEEE, 2007, pp. 3565–3572
2007
Earlier work this paper cites.
G. Klein and D. Murray, “Parallel tracking and mapping for small ar workspaces,” in 2007 6th IEEE and ACM international symposium on mixed and augmented reality . IEEE, 2007, pp. 225–234
2007
Earlier work this paper cites.
B. Siciliano, O. Khatib, and T. Kröger, Springer handbook of robotics . Springer, 2008, vol. 200
2008
Earlier work this paper cites.
P. R. Wurman, R. D’Andrea, and M. Mountz, “Coordinating hundreds of cooperative, autonomous vehicles in warehouses,” AI magazine , vol. 29, no. 1, pp. 9–9, 2008
2008
Earlier work this paper cites.
M. Raibert, K. Blankespoor, G. Nelson, and R. Playter, “Bigdog, the rough-terrain quadruped robot,” IFAC Proceedings Volumes , vol. 41, no. 2, pp. 10 822–10 825, 2008
2008
Earlier work this paper cites.
M. R. Cutkosky, R. D. Howe, and W. R. Provancher, “Force and tactile sensors,” Springer Handbook of Robotics , pp. 455–476, 2008
2008
Earlier work this paper cites.
S. J. Lederman and R. L. Klatzky, “Haptic perception: A tutorial,” Attention, Perception, & Psychophysics , vol. 71, no. 7, pp. 1439–1459, 2009
2009
Earlier work this paper cites.
R. A. Newcombe, S. J. Lovegrove, and A. J. Davison, “Dtam: Dense tracking and mapping in real-time,” in 2011 international conference on computer vision . IEEE, 2011, pp. 2320–2327
2011
Earlier work this paper cites.
Y. Jiang, S. Moseson, and A. Saxena, “Efficient grasping from rgbd images: Learning using a new rectangle representation,” in IEEE International Conference on Robotics and Automation . IEEE, 2011, pp. 3304–3311
2011
Earlier work this paper cites.
K. Sreenath, H.-W. Park, I. Poulakakis, and J. W. Grizzle, “A compliant hybrid zero dynamics controller for stable, efficient and fast bipedal walking on mabel,” The International Journal of Robotics Research , vol. 30, no. 9, pp. 1170–1193, 2011
2011
Earlier work this paper cites.
E. Todorov, T. Erez, and Y. Tassa, “Mujoco: A physics engine for model-based control,” in IEEE/RSJ International Conference on Intelligent Robots and Systems , 2012, pp. 5026–5033
2012
Earlier work this paper cites.
J. A. Fishel and G. E. Loeb, “Sensing tactile microvibrations with the biotac—comparison with human sensitivity,” in 2012 4th IEEE RAS & EMBS international conference on biomedical robotics and biomechatronics (BioRob) . IEEE, 2012, pp. 1122–1127
2012
Earlier work this paper cites.
E. Rohmer, S. P. Singh, and M. Freese, “V-rep: A versatile and scalable robot simulation framework,” in IEEE/RSJ international conference on intelligent robots and systems , 2013, pp. 1321–1326
2013
Earlier work this paper cites.
R. F. Salas-Moreno, R. A. Newcombe, H. Strasdat, P. H. Kelly, and A. J. Davison, “Slam++: Simultaneous localisation and mapping at the level of objects,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2013, pp. 1352–1359
2013
Earlier work this paper cites.
J. Engel, T. Schöps, and D. Cremers, “Lsd-slam: Large-scale direct monocular slam,” in European conference on computer vision . Springer, 2014, pp. 834–849
2014
Earlier work this paper cites.
S. Stassi, V. Cauda, G. Canavese, and C. F. Pirri, “Flexible tactile sensing based on piezoresistive composites: A review,” Sensors , vol. 14, no. 3, pp. 5296–5332, 2014
2014
Earlier work this paper cites.
S. Feng, E. Whitman, X. Xinjilefu, and C. G. Atkeson, “Optimization based full body control for the atlas robot,” in 2014 IEEE-RAS International Conference on Humanoid Robots , 2014, pp. 120–127
2014
Earlier work this paper cites.
F. Tanaka, K. Isshiki, F. Takahashi, M. Uekusa, R. Sei, and K. Hayashi, “Pepper learns together with children: Development of an educational application,” in IEEE-RAS 15th International Conference on Humanoid Robots , 2015, pp. 270–275
2015
Earlier work this paper cites.
R. Mur-Artal, J. M. M. Montiel, and J. D. Tardos, “Orb-slam: a versatile and accurate monocular slam system,” IEEE transactions on robotics , vol. 31, no. 5, pp. 1147–1163, 2015
2015
Earlier work this paper cites.
H. Su, S. Maji, E. Kalogerakis, and E. Learned-Miller, “Multi-view convolutional neural networks for 3d shape recognition,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 945–953
2015
Earlier work this paper cites.
D. Maturana and S. Scherer, “Voxnet: A 3d convolutional neural network for real-time object recognition,” in IEEE/RSJ International Conference on Intelligent Robots and Systems , 2015, pp. 922–928
2015
Earlier work this paper cites.
Z. Kappassov, J.-A. Corrales, and V. Perdereau, “Tactile sensing in dexterous robot hands,” Robotics and Autonomous Systems , vol. 74, pp. 195–220, 2015
2015
Earlier work this paper cites.
S. Maniatopoulos, P. Schillinger, V. Pong, D. C. Conner, and H. Kress-Gazit, “Reactive high-level behavior synthesis for an atlas humanoid robot,” in IEEE international conference on robotics and automation . IEEE, 2016, pp. 4192–4199
2016
Earlier work this paper cites.
E. Coumans and Y. Bai, “Pybullet, a python module for physics simulation for games, robotics and machine learning,” 2016
2016
Earlier work this paper cites.
L. Pinto, D. Gandhi, Y. Han, Y.-L. Park, and A. Gupta, “The curious robot: Learning visual representations via physical interactions,” in European Conference on Computer Vision , 2016, pp. 3–18
2016
Earlier work this paper cites.
M. Hutter, C. Gehring, D. Jud, A. Lauber, C. D. Bellicoso, V. Tsounis, J. Hwangbo, K. Bodie, P. Fankhauser, M. Bloesch et al. , “Anymal-a highly mobile and dynamic quadrupedal robot,” in IEEE/RSJ International Conference on Intelligent Robots and Systems , 2016, pp. 38–44
2016
Earlier work this paper cites.
C. Gehring, S. Coros, M. Hutter, C. D. Bellicoso, H. Heijnen, R. Diethelm, M. Bloesch, P. Fankhauser, J. Hwangbo, M. Hoepflinger et al. , “Practice makes perfect: An optimization-based approach to controlling agile motions for a quadruped robot,” IEEE Robotics & Automation Magazine , vol. 23, no. 1, pp. 34–43, 2016
2016
Earlier work this paper cites.
S. Shah, D. Dey, C. Lovett, and A. Kapoor, “Airsim: High-fidelity visual and physical simulation for autonomous vehicles,” in Field and Service Robotics , 2017
2017
Earlier work this paper cites.
E. Kolve, R. Mottaghi, D. Gordon, Y. Zhu, A. Gupta, and A. Farhadi, “Ai2-thor: An interactive 3d environment for visual ai,” arXiv: Computer Vision and Pattern Recognition,arXiv: Computer Vision and Pattern Recognition , Dec 2017
2017
Earlier work this paper cites.
A. Chang, A. Dai, T. Funkhouser, M. Halber, M. Niebner, M. Savva, S. Song, A. Zeng, and Y. Zhang, “Matterport3d: Learning from rgb-d data in indoor environments,” in 2017 International Conference on 3D Vision (3DV) , Oct 2017
2017
Earlier work this paper cites.
S. Song, F. Yu, A. Zeng, A. X. Chang, M. Savva, and T. Funkhouser, “Semantic scene completion from a single depth image,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 1746–1754
2017
Earlier work this paper cites.
C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 652–660
2017
Earlier work this paper cites.
C. R. Qi, L. Yi, H. Su, and L. J. Guibas, “Pointnet++: Deep hierarchical feature learning on point sets in a metric space,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
W. Yuan, S. Dong, and E. H. Adelson, “Gelsight: High-resolution robot tactile sensors for estimating geometry and force,” Sensors (Basel, Switzerland) , vol. 17, 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
S. Dong, W. Yuan, and E. H. Adelson, “Improved gelsight tactile sensor for measuring geometry and slip,” in IEEE/RSJ International Conference on Intelligent Robots and Systems , 2017, pp. 137–144
2017
Earlier work this paper cites.
W. Yuan, S. Dong, and E. H. Adelson, “Gelsight: High-resolution robot tactile sensors for estimating geometry and force,” Sensors , vol. 17, no. 12, p. 2762, 2017
2017
Earlier work this paper cites.
W. Yuan, S. Wang, S. Dong, and E. Adelson, “Connecting look and feel: Associating the visual and tactile properties of physical materials,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 5580–5588
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
A. Dai, A. X. Chang, M. Savva, M. Halber, T. Funkhouser, and M. Nießner, “Scannet: Richly-annotated 3d reconstructions of indoor scenes,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 5828–5839
2017
Earlier work this paper cites.
J. Varley, C. DeChant, A. Richardson, J. Ruales, and P. Allen, “Shape completion enabled robotic grasping,” in IEEE/RSJ International Conference on Intelligent Robots and Systems , 2017, pp. 2442–2447
2017
Earlier work this paper cites.
J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba, and P. Abbeel, “Domain randomization for transferring deep neural networks from simulation to the real world,” in IEEE/RSJ International Conference on Intelligent Robots and Systems , 2017, pp. 23–30
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Q. Nguyen, A. Agrawal, X. Da, W. C. Martin, H. Geyer, J. W. Grizzle, and K. Sreenath, “Dynamic walking on randomly-varying discrete terrain with one-step preview.” in Robotics: Science and Systems , vol. 2, no. 3, 2017, pp. 384–399
2017
Earlier work this paper cites.
R. Antonova, A. Rai, and C. G. Atkeson, “Deep kernels for optimizing locomotion controllers,” in Conference on Robot Learning . PMLR, 2017, pp. 47–56
2017
Earlier work this paper cites.
S. Shigemi, A. Goswami, and P. Vadakkepat, “Asimo and humanoid robot research at honda,” Humanoid robotics: A reference , vol. 55, p. 90, 2018
2018
Earlier work this paper cites.
R. K. Katzschmann, J. DelPreto, R. MacCurdy, and D. Rus, “Exploration of underwater life with an acoustically controlled soft robotic fish,” Science Robotics , vol. 3, no. 16, p. eaar3449, 2018
2018
Earlier work this paper cites.
P. Anderson, Q. Wu, D. Teney, J. Bruce, M. Johnson, N. Sunderhauf, I. Reid, S. Gould, and A. van den Hengel, “Vision-and-language navigation: Interpreting visually-grounded navigation instructions in real environments,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , Jun 2018
2018
Earlier work this paper cites.
X. Puig, K. Ra, M. Boben, J. Li, T. Wang, S. Fidler, and A. Torralba, “Virtualhome: Simulating household activities via programs,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , Jun 2018
2018
Earlier work this paper cites.
L. Nicholson, M. Milford, and N. Sünderhauf, “Quadricslam: Dual quadrics from object detections as landmarks in object-oriented slam,” IEEE Robotics and Automation Letters , vol. 4, no. 1, pp. 1–8, 2018
2018
Earlier work this paper cites.
C. Yu, Z. Liu, X.-J. Liu, F. Xie, Y. Yang, Q. Wei, and Q. Fei, “Ds-slam: A semantic visual slam towards dynamic environments,” in IEEE/RSJ International Conference on Intelligent Robots and Systems , 2018, pp. 1168–1174
2018
Earlier work this paper cites.
B. Bescos, J. M. Fácil, J. Civera, and J. Neira, “Dynaslam: Tracking, mapping, and inpainting in dynamic scenes,” IEEE Robotics and Automation Letters , vol. 3, no. 4, pp. 4076–4083, 2018
2018
Earlier work this paper cites.
B. Graham, M. Engelcke, and L. Van Der Maaten, “3d semantic segmentation with submanifold sparse convolutional networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 9224–9232
2018
Earlier work this paper cites.
D. Jayaraman and K. Grauman, “Learning to look around: Intelligently exploring unseen environments for unknown tasks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 1238–1247
2018
Earlier work this paper cites.
D. Droeschel and S. Behnke, “Efficient continuous-time slam for 3d lidar-based online mapping,” in IEEE International Conference on Robotics and Automation . IEEE, 2018, pp. 5000–5007
2018
Earlier work this paper cites.
Y. Zhou and O. Tuzel, “Voxelnet: End-to-end learning for point cloud based 3d object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 4490–4499
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
X. E. Wang, W. Xiong, H. Wang, and W. Y. Wang, “Look before you leap: Bridging model-free and model-based reinforcement learning for planned-ahead vision-and-language navigation,” in European Conference on Computer Vision , 2018
2018
Earlier work this paper cites.
B. Ward-Cherrier, N. Pestell, L. Cramphorn, B. Winstone, M. E. Giannaccini, J. Rossiter, and N. F. Lepora, “The tactip family: Soft optical tactile sensors with 3d-printed biomimetic morphologies,” Soft robotics , vol. 5, no. 2, pp. 216–227, 2018
2018
Earlier work this paper cites.
S. Wang, J. Wu, X. Sun, W. Yuan, W. T. Freeman, J. B. Tenenbaum, and E. H. Adelson, “3d shape perception from monocular vision, touch, and shape priors,” in IEEE/RSJ International Conference on Intelligent Robots and Systems , 2018, pp. 1606–1613
2018
Earlier work this paper cites.
A. Das, S. Datta, G. Gkioxari, S. Lee, D. Parikh, and D. Batra, “Embodied question answering,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 1–10
2018
Earlier work this paper cites.
D. Gordon, A. Kembhavi, M. Rastegari, J. Redmon, D. Fox, and A. Farhadi, “Iqa: Visual question answering in interactive environments,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2018, pp. 4089–4098
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
A. Das, G. Gkioxari, S. Lee, D. Parikh, and D. Batra, “Neural modular control for embodied question answering,” in Conference on Robot Learning . PMLR, 2018, pp. 53–62
2018
Earlier work this paper cites.
A. Depierre, E. Dellandréa, and L. Chen, “Jacquard: A large scale dataset for robotic grasp detection,” in IEEE/RSJ International Conference on Intelligent Robots and Systems , 2018, pp. 3511–3516
2018
Earlier work this paper cites.
D. Ha and J. Schmidhuber, “World models,” arXiv preprint arXiv:1803.10122 , 2018
2018
Earlier work this paper cites.
J. Matas, S. James, and A. J. Davison, “Sim-to-real reinforcement learning for deformable object manipulation,” in Conference on Robot Learning . PMLR, 2018, pp. 734–743
2018
Earlier work this paper cites.
G. Bledt, M. J. Powell, B. Katz, J. Di Carlo, P. M. Wensing, and S. Kim, “Mit cheetah 3: Design and control of a robust, dynamic quadruped robot,” in IEEE/RSJ International Conference on Intelligent Robots and Systems , 2018, pp. 2245–2252
2018
Earlier work this paper cites.
M. Kennedy, K. Schmeckpeper, D. Thakur, C. Jiang, V. Kumar, and K. Daniilidis, “Autonomous precision pouring from unknown containers,” IEEE Robotics and Automation Letters , vol. 4, no. 3, pp. 2317–2324, 2019
2019
Earlier work this paper cites.
D. J. Agravante, A. Cherubini, A. Sherikov, P.-B. Wieber, and A. Kheddar, “Human-humanoid collaborative carrying,” IEEE Transactions on Robotics , vol. 35, no. 4, pp. 833–846, 2019
2019
Earlier work this paper cites.
N. R. Sinatra, C. B. Teeple, D. M. Vogt, K. K. Parker, D. F. Gruber, and R. J. Wood, “Ultragentle manipulation of delicate structures using a soft robotic gripper,” Science Robotics , vol. 4, no. 33, p. eaax5425, 2019
2019
Earlier work this paper cites.
M. Savva, A. Kadian, O. Maksymets, Y. Zhao, E. Wijmans, B. Jain, J. Straub, J. Liu, V. Koltun, J. Malik, D. Parikh, and D. Batra, “Habitat: A platform for embodied ai research,” in IEEE/CVF International Conference on Computer Vision , Oct 2019
2019
Earlier work this paper cites.
S. Yang and S. Scherer, “Cubeslam: Monocular 3-d object slam,” IEEE Transactions on Robotics , vol. 35, no. 4, pp. 925–938, 2019
2019
Earlier work this paper cites.
J. Zhang, M. Gui, Q. Wang, R. Liu, J. Xu, and S. Chen, “Hierarchical topic model based object association for semantic slam,” IEEE transactions on visualization and computer graphics , vol. 25, no. 11, pp. 3052–3062, 2019
2019
Earlier work this paper cites.
A. H. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang, and O. Beijbom, “Pointpillars: Fast encoders for object detection from point clouds,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 12 697–12 705
2019
Earlier work this paper cites.
C. Choy, J. Gwak, and S. Savarese, “4d spatio-temporal convnets: Minkowski convolutional neural networks,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 3075–3084
2019
Earlier work this paper cites.
X. Wang, Q. Huang, A. Celikyilmaz, J. Gao, D. Shen, Y.-F. Wang, W. Y. Wang, and L. Zhang, “Reinforced cross-modal matching and self-supervised imitation learning for vision-language navigation,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 6629–6638
2019
Earlier work this paper cites.
Z. Ding, X. Han, and M. Niethammer, “Votenet: A deep learning label fusion method for multi-atlas segmentation,” in Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part III 22 . Springer, 2019, pp. 202–210
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
V. Jain, G. Magalhaes, A. Ku, A. Vaswani, E. Ie, and J. Baldridge, “Stay on the path: Instruction fidelity in vision-and-language navigation,” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics , Jan 2019
2019
Earlier work this paper cites.
H. Chen, A. Suhr, D. Misra, N. Snavely, and Y. Artzi, “Touchdown: Natural language navigation and spatial reasoning in visual street environments,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , Jun 2019
2019
Earlier work this paper cites.
P. Ruppel, Y. Jonetzko, M. Görner, N. Hendrich, and J. Zhang, “Simulation of the syntouch biotac sensor,” in Intelligent Autonomous Systems 15: Proceedings of the 15th International Conference IAS-15 . Springer, 2019, pp. 374–387
2019
Earlier work this paper cites.
B. Sundaralingam, A. S. Lambert, A. Handa, B. Boots, T. Hermans, S. Birchfield, N. Ratliff, and D. Fox, “Robust learning of tactile force estimation through robot interaction,” in International Conference on Robotics and Automation , 2019, pp. 9035–9042
2019
Earlier work this paper cites.
N. F. Lepora, A. Church, C. De Kerckhove, R. Hadsell, and J. Lloyd, “From pixels to percepts: Highly robust edge perception and contour following using deep learning and an optical biomimetic tactile sensor,” IEEE Robotics and Automation Letters , vol. 4, no. 2, pp. 2101–2107, 2019
2019
Earlier work this paper cites.
M. Bauza, O. Canal, and A. Rodriguez, “Tactile mapping and localization from high-resolution tactile imprints,” in International Conference on Robotics and Automation , 2019, pp. 3811–3817
2019
Earlier work this paper cites.
M. Polic, I. Krajacic, N. Lepora, and M. Orsag, “Convolutional autoencoder for feature extraction in tactile sensing,” IEEE Robotics and Automation Letters , vol. 4, no. 4, pp. 3671–3678, 2019
2019
Earlier work this paper cites.
J. Lin, R. Calandra, and S. Levine, “Learning to identify object instances by touch: Tactile recognition via multimodal matching,” in International Conference on Robotics and Automation , 2019, pp. 3644–3650
2019
Earlier work this paper cites.
L. Yu, X. Chen, G. Gkioxari, M. Bansal, T. L. Berg, and D. Batra, “Multi-target embodied question answering,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 6309–6318
2019
Earlier work this paper cites.
E. Wijmans, S. Datta, O. Maksymets, A. Das, G. Gkioxari, S. Lee, I. Essa, D. Parikh, and D. Batra, “Embodied question answering in photorealistic environments with point cloud perception,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 6659–6668
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
A. Mousavian, C. Eppner, and D. Fox, “6-dof graspnet: Variational grasp generation for object manipulation,” in Proceedings of the IEEE/CVF international conference on computer vision , 2019, pp. 2901–2910
2019
Earlier work this paper cites.
Q. Nguyen, M. J. Powell, B. Katz, J. Di Carlo, and S. Kim, “Optimized jumping on the mit cheetah 3 robot,” in International Conference on Robotics and Automation , 2019, pp. 7448–7454
2019
Earlier work this paper cites.
C. Li, S. Zhu, Z. Sun, and J. Rogers, “Bas optimized elm for kuka iiwa robot learning,” IEEE Transactions on Circuits and Systems II: Express Briefs , vol. 68, no. 6, pp. 1987–1991, 2020
2020
Earlier work this paper cites.
A. Bouman, M. F. Ginting, N. Alatur, M. Palieri, D. D. Fan, T. Touma, T. Pailevanian, S.-K. Kim, K. Otsu, J. Burdick et al. , “Autonomous spot: Long-range autonomous exploration of extreme environments with legged locomotion,” in IEEE/RSJ International Conference on Intelligent Robots and Systems , 2020, pp. 2518–2525
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
C. Wang, Q. Zhang, Q. Tian, S. Li, X. Wang, D. Lane, Y. Petillot, and S. Wang, “Learning mobile manipulation through deep reinforcement learning,” Sensors , vol. 20, no. 3, p. 939, 2020
2020
Earlier work this paper cites.
F. Xiang, Y. Qin, K. Mo, Y. Xia, H. Zhu, F. Liu, M. Liu, H. Jiang, Y. Yuan, H. Wang, L. Yi, A. X. Chang, L. J. Guibas, and H. Su, “Sapien: A simulated part-based interactive environment,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , Jun 2020
2020
Earlier work this paper cites.
X. Zhang, J. Lai, D. Xu, H. Li, and M. Fu, “2d lidar-based slam and path planning for indoor rescue using mobile robots,” Journal of Advanced Transportation , vol. 2020, no. 1, p. 8867937, 2020
2020
Earlier work this paper cites.
J. Ruan, B. Li, Y. Wang, and Z. Fang, “Gp-slam+: real-time 3d lidar slam based on improved regionalized gaussian process map reconstruction,” in IEEE/RSJ International Conference on Intelligent Robots and Systems , 2020, pp. 5171–5178
2020
Earlier work this paper cites.
V. Mittal, “Attngrounder: Talking to cars with attention,” in ECCV . Springer, 2020, pp. 62–73
2020
Earlier work this paper cites.
D. Z. Chen, A. X. Chang, and M. Nießner, “Scanrefer: 3d object localization in rgb-d scans using natural language,” in European conference on computer vision . Springer, 2020, pp. 202–221
2020
Earlier work this paper cites.
P. Achlioptas, A. Abdelreheem, F. Xia, M. Elhoseiny, and L. Guibas, “Referit3d: Neural listeners for fine-grained 3d object identification in real-world scenes,” in ECCV . Springer, 2020, pp. 422–440
2020
Earlier work this paper cites.
L. Jiang, H. Zhao, S. Shi, S. Liu, C.-W. Fu, and J. Jia, “Pointgroup: Dual-set point grouping for 3d instance segmentation,” in Proceedings of the IEEE/CVF conference on computer vision and Pattern recognition , 2020, pp. 4867–4876
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
J. Krantz, E. Wijmans, A. Majumdar, D. Batra, and S. Lee, Beyond the Nav-Graph: Vision-and-Language Navigation in Continuous Environments , Jan 2020, p. 104–120
2020
Earlier work this paper cites.
Y. Qi, Q. Wu, P. Anderson, X. Wang, W. Y. Wang, C. Shen, and A. van den Hengel, “Reverie: Remote embodied visual referring expression in real indoor environments,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , Jun 2020
2020
Earlier work this paper cites.
M. Shridhar, J. Thomason, D. Gordon, Y. Bisk, W. Han, R. Mottaghi, L. Zettlemoyer, and D. Fox, “Alfred: A benchmark for interpreting grounded instructions for everyday tasks,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , Jun 2020
2020
Earlier work this paper cites.
J. Thomason, M. Murray, M. Cakmak, and L. Zettlemoyer, “Vision-and-dialog navigation,” in Conference on Robot Learning . PMLR, 2020, pp. 394–406
2020
Earlier work this paper cites.
Y. Hong, C. Rodriguez, Y. Qi, Q. Wu, and S. Gould, “Language and visual entity relationship graph for agent navigation,” vol. 33, 2020, pp. 7685–7696
2020
Earlier work this paper cites.
W. Zhang, C. Ma, Q. Wu, and X. Yang, “Language-guided navigation via cross-modal grounding and alternate adversarial learning,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 31, pp. 3469–3481, 2020
2020
Earlier work this paper cites.
M. Lambeta, P.-W. Chou, S. Tian, B. Yang, B. Maloon, V. R. Most, D. Stroud, R. Santos, A. Byagowi, G. Kammerer, D. Jayaraman, and R. Calandra, “Digit: A novel design for a low-cost compact high-resolution tactile sensor with application to in-hand manipulation,” IEEE Robotics and Automation Letters , vol. 5, no. 3, p. 3838–3845, Jul. 2020
2020
Earlier work this paper cites.
D. F. Gomes, Z. Lin, and S. Luo, “Geltip: A finger-shaped optical tactile sensor for robotic manipulation,” in IEEE/RSJ International Conference on Intelligent Robots and Systems , 2020, pp. 9903–9909
2020
Cited alongside, same era.
K. Patel, S. Iba, and N. Jamali, “Deep tactile experience: Estimating tactile sensor output from depth sensor data,” in IEEE/RSJ International Conference on Intelligent Robots and Systems , 2020, pp. 9846–9853
2020
Cited alongside, same era.
M. Lambeta, P.-W. Chou, S. Tian, B. Yang, B. Maloon, V. R. Most, D. Stroud, R. Santos, A. Byagowi, G. Kammerer et al. , “Digit: A novel design for a low-cost compact high-resolution tactile sensor with application to in-hand manipulation,” IEEE Robotics and Automation Letters , vol. 5, no. 3, pp. 3838–3845, 2020
2020
Cited alongside, same era.
E. Smith, R. Calandra, A. Romero, G. Gkioxari, D. Meger, J. Malik, and M. Drozdzal, “3d shape reconstruction from vision and touch,” Advances in Neural Information Processing Systems , vol. 33, pp. 14 193–14 206, 2020
S. Suresh, Z. Si, S. Anderson, M. Kaess, and M. Mukadam, “Midastouch: Monte-carlo inference over distributions across sliding touch,” in Conference on Robot Learning , 2023, pp. 319–331
2023
Later among the works it cites.
R. Gao, Y. Dou, H. Li, T. Agarwal, J. Bohg, Y. Li, L. Fei-Fei, and J. Wu, “The objectfolder benchmark: Multisensory learning with neural and real objects,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 17 276–17 286
2023
Later among the works it cites.
S. Zhong, A. Albini, O. P. Jones, P. Maiolino, and I. Posner, “Touching a nerf: Leveraging neural radiance fields for tactile sensory data generation,” in Conference on Robot Learning . PMLR, 2023, pp. 1618–1628
2023
Later among the works it cites.
2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2020
Cited alongside, same era.
R. Gao, T. Taunyazov, Z. Lin, and Y. Wu, “Supervised autoencoder joint learning on heterogeneous tactile sensory data: Improving material classification performance,” in IEEE/RSJ International Conference on Intelligent Robots and Systems , 2020, pp. 10 907–10 913
2020
Cited alongside, same era.
M. A. Lee, Y. Zhu, P. Zachares, M. Tan, K. Srinivasan, S. Savarese, L. Fei-Fei, A. Garg, and J. Bohg, “Making sense of vision and touch: Learning multimodal representations for contact-rich tasks,” IEEE Transactions on Robotics , vol. 36, no. 3, pp. 582–596, 2020
2020
Cited alongside, same era.
Y. Wu, L. Jiang, and Y. Yang, “Revisiting embodiedqa: A simple baseline and beyond,” IEEE Transactions on Image Processing , vol. 29, pp. 3984–3992, 2020
2020
Cited alongside, same era.
S. Tan, W. Xiang, H. Liu, D. Guo, and F. Sun, “Multi-agent embodied question answering in interactive environments,” in European Conference on Computer Vision , 2020, pp. 663–678
2020
Cited alongside, same era.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al. , “Language models are few-shot learners,” Advances in neural information processing systems , vol. 33, pp. 1877–1901, 2020
2020
Cited alongside, same era.
H.-S. Fang, C. Wang, M. Gou, and C. Lu, “Graspnet-1billion: A large-scale benchmark for general object grasping,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 11 444–11 453
2020
Cited alongside, same era.
M. Shridhar, J. Thomason, D. Gordon, Y. Bisk, W. Han, R. Mottaghi, L. Zettlemoyer, and D. Fox, “Alfred: A benchmark for interpreting grounded instructions for everyday tasks,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 10 740–10 749
2020
Cited alongside, same era.
2020
Cited alongside, same era.
F. Yang, J. Zhang, and A. Owens, “Generating visual scenes from touch,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 22 070–22 080
2023
Later among the works it cites.
C. Lin, Z. Lin, S. Wang, and H. Xu, “Dtact: A vision-based tactile sensor that measures high-resolution 3d geometry directly from darkness,” in IEEE International Conference on Robotics and Automation . IEEE, 2023, pp. 10 359–10 366
2023
Later among the works it cites.
Y. Chen, A. E. Tekden, M. P. Deisenroth, and Y. Bekiroglu, “Sliding touch-based exploration for modeling unknown object shape with multi-fingered hands,” in IEEE/RSJ International Conference on Intelligent Robots and Systems , 2023, pp. 8943–8950
2023
Later among the works it cites.
M. Comi, A. Church, K. Li, L. Aitchison, and N. F. Lepora, “Implicit neural representation for 3d shape reconstruction using vision-based tactile sensing,” 2023
2023
Later among the works it cites.
Y. Gao, S. Matsuoka, W. Wan, T. Kiyokawa, K. Koyama, and K. Harada, “In-hand pose estimation using hand-mounted rgb cameras and visuotactile sensors,” IEEE Access , vol. 11, pp. 17 218–17 232, 2023
2023
Later among the works it cites.
G. M. Caddeo, N. A. Piga, F. Bottarel, and L. Natale, “Collision-aware in-hand 6d object pose estimation using multiple vision-based tactile sensors,” in IEEE International Conference on Robotics and Automation , 2023, pp. 719–725
2023
Later among the works it cites.
H. Qi, B. Yi, S. Suresh, M. Lambeta, Y. Ma, R. Calandra, and J. Malik, “General in-hand object rotation with vision and touch,” in Proceedings of The 7th Conference on Robot Learning , ser. Proceedings of Machine Learning Research, J. Tan, M. Toussaint, and K. Darvish, Eds., vol. 229. PMLR, 06–09 Nov 2023, pp. 2549–2564
2023
Later among the works it cites.
M. Yang, Y. Lin, A. Church, J. Lloyd, D. Zhang, D. A. Barton, and N. F. Lepora, “Sim-to-real model-based and model-free deep reinforcement learning for tactile pushing,” IEEE Robotics and Automation Letters , 2023
2023
Later among the works it cites.
X. Jing, K. Qian, T. Jianu, and S. Luo, “Unsupervised adversarial domain adaptation for sim-to-real transfer of tactile images,” IEEE Transactions on Instrumentation and Measurement , 2023
2023
Later among the works it cites.
G. Cao, J. Jiang, D. Bollegala, and S. Luo, “Learn from incomplete tactile data: Tactile representation learning with masked autoencoders,” in IEEE/RSJ International Conference on Intelligent Robots and Systems , 2023, pp. 10 800–10 805
2023
Later among the works it cites.
2023
Later among the works it cites.
X. Ma, S. Yong, Z. Zheng, Q. Li, Y. Liang, S.-C. Zhu, and S. Huang, “Sqa3d: Situated question answering in 3d scenes,” in International Conference on Learning Representations , 2023
2023
Later among the works it cites.
S. Tan, M. Ge, D. Guo, H. Liu, and F. Sun, “Knowledge-based embodied question answering,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
R. Newbury, M. Gu, L. Chumbley, A. Mousavian, C. Eppner, J. Leitner, J. Bohg, A. Morales, T. Asfour, D. Kragic et al. , “Deep learning approaches to grasp synthesis: A review,” IEEE Transactions on Robotics , 2023
2023
Later among the works it cites.
J. Xu, S. Jin, Y. Lei, Y. Zhang, and L. Zhang, “Reasoning tuning grasp: Adapting multi-modal large language models for robotic grasping,” in 2nd Workshop on Language and Robot Learning: Language as Grounding , 2023
2023
Later among the works it cites.
W. Shen, G. Yang, A. Yu, J. Wong, L. P. Kaelbling, and P. Isola, “Distilled feature fields enable few-shot language-guided manipulation,” in 7th Annual Conference on Robot Learning , 2023
2023
Later among the works it cites.
H.-S. Fang, C. Wang, H. Fang, M. Gou, J. Liu, H. Yan, W. Liu, Y. Xie, and C. Lu, “Anygrasp: Robust and efficient grasp perception in spatial and temporal domains,” IEEE Transactions on Robotics , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
A. Brohan, Y. Chebotar, C. Finn, K. Hausman, A. Herzog, D. Ho, J. Ibarz, A. Irpan, E. Jang, R. Julian et al. , “Do as i can, not as i say: Grounding language in robotic affordances,” in Conference on robot learning . PMLR, 2023, pp. 287–318
2023
Later among the works it cites.
Y. Chebotar, Q. Vuong, K. Hausman, F. Xia, Y. Lu, A. Irpan, A. Kumar, T. Yu, A. Herzog, K. Pertsch et al. , “Q-transformer: Scalable offline reinforcement learning via autoregressive q-functions,” in Conference on Robot Learning . PMLR, 2023, pp. 3909–3928
2023
Later among the works it cites.
D. Driess, F. Xia, M. S. Sajjadi, C. Lynch, A. Chowdhery, B. Ichter, A. Wahid, J. Tompson, Q. Vuong, T. Yu et al. , “Palm-e: an embodied multimodal language model,” in Proceedings of the 40th International Conference on Machine Learning , 2023, pp. 8469–8488
2023
Later among the works it cites.
B. Zitkovich, T. Yu, S. Xu, P. Xu, T. Xiao, F. Xia, J. Wu, P. Wohlhart, S. Welker, A. Wahid et al. , “Rt-2: Vision-language-action models transfer web knowledge to robotic control,” in Conference on Robot Learning . PMLR, 2023, pp. 2165–2183
2023
Later among the works it cites.
Q. Vuong, S. Levine, H. R. Walke, K. Pertsch, A. Singh, R. Doshi, C. Xu, J. Luo, L. Tan, D. Shah et al. , “Open x-embodiment: Robotic learning datasets and rt-x models,” in Towards Generalist Robots: Learning Paradigms for Scalable Skill Acquisition@ CoRL2023 , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
W. Huang, F. Xia, T. Xiao, H. Chan, J. Liang, P. Florence, A. Zeng, J. Tompson, I. Mordatch, Y. Chebotar et al. , “Inner monologue: Embodied reasoning through planning with language models,” in Conference on Robot Learning . PMLR, 2023, pp. 1769–1782
2023
Later among the works it cites.
S. Yao, J. Zhao, D. Yu, N. Du, I. Shafran, K. Narasimhan, and Y. Cao, “React: Synergizing reasoning and acting in language models,” in International Conference on Learning Representations (ICLR) , 2023
2023
Later among the works it cites.
G. Sarch, Y. Wu, M. Tarr, and K. Fragkiadaki, “Open-ended instructable embodied agents with memory-augmented large language models,” in Findings of the Association for Computational Linguistics: EMNLP 2023 , 2023, pp. 3468–3500
2023
Later among the works it cites.
2023
Later among the works it cites.
I. Singh, V. Blukis, A. Mousavian, A. Goyal, D. Xu, J. Tremblay, D. Fox, J. Thomason, and A. Garg, “Progprompt: Generating situated robot task plans using large language models,” in IEEE International Conference on Robotics and Automation , 2023, pp. 11 523–11 530
2023
Later among the works it cites.
S. Vemprala, R. Bonatti, A. F. C. Bucker, and A. Kapoor, “Chatgpt for robotics: Design principles and model abilities,” IEEE Access , vol. 12, pp. 55 682–55 696, 2023
2023
Later among the works it cites.
A. Zeng, M. Attarian, K. M. Choromanski, A. Wong, S. Welker, F. Tombari, A. Purohit, M. S. Ryoo, V. Sindhwani, J. Lee et al. , “Socratic models: Composing zero-shot multimodal reasoning with language,” in The Eleventh International Conference on Learning Representations , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
K. Rana, J. Haviland, S. Garg, J. Abou-Chakra, I. D. Reid, and N. Sünderhauf, “Sayplan: Grounding large language models using 3d scene graphs for scalable task planning,” in Conference on Robot Learning , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
X. Zhao, M. Li, C. Weber, M. B. Hafez, and S. Wermter, “Chat with the environment: Interactive multimodal perception using large language models,” in IEEE/RSJ International Conference on Intelligent Robots and Systems , 2023, pp. 3590–3596
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
H. Geng, H. Xu, C. Zhao, C. Xu, L. Yi, S. Huang, and H. Wang, “Gapartnet: Cross-category domain-generalizable object perception and manipulation via generalizable and actionable parts,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 7081–7091
2023
Later among the works it cites.
2023
Later among the works it cites.
Y. Liu, G. Li, and L. Lin, “Cross-modal causal relational reasoning for event-level visual question answering,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 45, no. 10, pp. 11 624–11 641, 2023
2023
Later among the works it cites.
S. V. Mehta, D. Patil, S. Chandar, and E. Strubell, “An empirical investigation of the role of pre-training in lifelong learning,” Journal of Machine Learning Research , vol. 24, no. 214, pp. 1–50, 2023
2023
Later among the works it cites.
L. Londoño, J. V. Hurtado, N. Hertz, P. Kellmeyer, S. Voeneky, and A. Valada, “Fairness and bias in robot learning,” Proceedings of the IEEE , 2024
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
S. Le Cleac’h, T. A. Howell, S. Yang, C.-Y. Lee, J. Zhang, A. Bishop, M. Schwager, and Z. Manchester, “Fast contact-implicit model predictive control,” IEEE Transactions on Robotics , 2024
2024
Closest in time.
Y. Tong, H. Liu, and Z. Zhang, “Advancements in humanoid robots: A comprehensive review and future prospects,” IEEE/CAA Journal of Automatica Sinica , vol. 11, no. 2, pp. 301–328, 2024
2024
Closest in time.
J. Xiang, T. Tao, Y. Gu, T. Shu, Z. Wang, Z. Yang, and Z. Hu, “Language models meet world models: Embodied experiences enhance language models,” Advances in neural information processing systems , vol. 36, 2024
2024
Closest in time.
Y. Yang, F.-Y. Sun, L. Weihs, E. VanderBilt, A. Herrasti, W. Han, J. Wu, N. Haber, R. Krishna, L. Liu et al. , “Holodeck: Language guided generation of 3d embodied ai environments,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 16 227–16 237
2024
Closest in time.
Y. Yang, B. Jia, P. Zhi, and S. Huang, “Physcene: Physically interactable 3d scene synthesis for embodied ai,” in Proceedings of Conference on Computer Vision and Pattern Recognition (CVPR) , 2024
2024
Closest in time.
C. Yan, D. Qu, D. Xu, B. Zhao, Z. Wang, D. Wang, and X. Li, “Gs-slam: Dense visual slam with 3d gaussian splatting,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 19 595–19 604
2024
Closest in time.
T. Wang, X. Mao, C. Zhu, R. Xu, R. Lyu, P. Li, X. Chen, W. Zhang, K. Chen, T. Xue et al. , “Embodiedscan: A holistic multi-modal 3d perception suite towards embodied ai,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 19 757–19 767
2024
Closest in time.
X. Wu, L. Jiang, P.-S. Wang, Z. Liu, X. Liu, Y. Qiao, W. Ouyang, T. He, and H. Zhao, “Point transformer v3: Simpler faster stronger,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 4840–4851
2024
Closest in time.
J. Huang, S. Yong, X. Ma, X. Linghu, P. Li, Y. Wang, Q. Li, S.-C. Zhu, B. Jia, and S. Huang, “An embodied generalist agent in 3d world,” in Proceedings of the International Conference on Machine Learning (ICML) , 2024
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
L. Fan, M. Liang, Y. Li, G. Hua, and Y. Wu, “Evidential active recognition: Intelligent and prudent open-world embodied perception,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 16 351–16 361
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
D. Zheng, S. Huang, L. Zhao, Y. Zhong, and L. Wang, “Towards learning a generalist model for embodied navigation,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , Jun 2024
2024
Closest in time.
J. Gao, X. Yao, and C. Xu, “Fast-slow test-time adaptation for online vision-and-language navigation,” 2024
2024
Closest in time.
Y. Long, X. Li, W. Cai, and H. Dong, “Discuss before moving: Visual language navigation via multi-expert discussions,” in IEEE International Conference on Robotics and Automation , 2024
2024
Closest in time.
R. D. M. S. C. L. Q. C. Liuyi Wang, Zongtao He, “Vision-and-language navigation via causal learning,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , Jun 2024
2024
Closest in time.
Y. Y. Rui Liu, Wenguan Wang, “Volumetric environment representation for vision-language navigation,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024 , Jun 2024
2024
Closest in time.
2024
Closest in time.
Z. Wang, X. Li, J. Yang, Y. Liu, J. Hu, M. Jiang, and S. Jiang, “Lookahead exploration with neural radiance representation for continuous vision-language navigation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024
2024
Closest in time.
D. An, H. Wang, W. Wang, Z. Wang, Y. Huang, K. He, and L. Wang, “Etpnav: Evolving topological planning for vision-language navigation in continuous environments,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2024
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
Y. Zhou, Z. Yan, Y. Yang, Z. Wang, P. Lu, P. F. Yuan, and B. He, “Bioinspired sensors and applications in intelligent robots: a review,” Robotic Intelligence and Automation , vol. 44, no. 2, pp. 215–228, 2024
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
Y. Dou, F. Yang, Y. Liu, A. Loquercio, and A. Owens, “Tactile-augmented radiance fields,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 26 529–26 539
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
A. Majumdar, A. Ajay, X. Zhang, P. Putta, S. Yenamandra, M. Henaff, S. Silwal, P. Mcvay, O. Maksymets, S. Arnaud et al. , “Openeqa: Embodied question answering in the era of foundation models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 16 488–16 498
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
Y. Mu, Q. Zhang, M. Hu, W. Wang, M. Ding, J. Jin, B. Wang, J. Dai, Y. Qiao, and P. Luo, “Embodiedgpt: Vision-language pre-training via embodied chain of thought,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Closest in time.
X. Li, M. Liu, H. Zhang, C. Yu, J. Xu, H. Wu, C. Cheang, Y. Jing, W. Zhang, H. Liu et al. , “Vision-language foundation models as effective robot imitators,” in The Twelfth International Conference on Learning Representations , 2024
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
J. Huang, S. Yong, X. Ma, X. Linghu, P. Li, Y. Wang, Q. Li, S.-C. Zhu, B. Jia, and S. Huang, “An embodied generalist agent in 3d world,” in Forty-first International Conference on Machine Learning , 2024
2024
Closest in time.
Z. Wu, Z. Wang, X. Xu, J. Lu, and H. Yan, “Embodied instruction following in unknown environments,” 2024
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
M. Liu, C. Xu, H. Jin, L. Chen, M. Varma T, Z. Xu, and H. Su, “One-2-3-45: Any single image to 3d mesh in 45 seconds without per-shape optimization,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.