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Embodied learning for object-centric robotic manipulation is a rapidly developing and challenging area in embodied AI.
J. J. Gibson, “The theory of affordances,” Hilldale, USA , vol. 1, no. 2, pp. 67–82, 1977
1977
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation , vol. 9, no. 8, pp. 1735–1780, 1997
1997
Earlier work this paper cites.
D. Rus, “In-hand dexterous manipulation of piecewise-smooth 3-d objects,” IJRR , vol. 18, no. 4, pp. 355–381, 1999
1999
Earlier work this paper cites.
A. M. Okamura et al. , “An overview of dexterous manipulation,” in ICRA , vol. 1, 2000, pp. 255–262
2000
Earlier work this paper cites.
X.-S. Gao et al. , “Complete solution classification for the perspective-three-point problem,” TPAMI , vol. 25, no. 8, pp. 930–943, 2003
2003
Earlier work this paper cites.
S. Schaal, J. Peters, J. Nakanishi, and A. Ijspeert, “Learning movement primitives,” in ISRR , 2005, pp. 561–572
2005
Earlier work this paper cites.
E. Frazzoli et al. , “Maneuver-based motion planning for nonlinear systems with symmetries,” TRO , vol. 21, no. 6, pp. 1077–1091, 2005
2005
Earlier work this paper cites.
B. D. Ziebart et al. , “Maximum entropy inverse reinforcement learning,” in AAAI , vol. 8, 2008, pp. 1433–1438
2008
Earlier work this paper cites.
A. Saxena et al. , “Robotic grasping of novel objects using vision,” IJRR , vol. 27, no. 2, pp. 157–173, 2008
2008
Earlier work this paper cites.
V. Lepetit, F. Moreno-Noguer, and P. Fua, “Epnp: An accurate o(n) solution to the pnp problem,” IJCV , vol. 81, pp. 155–166, 2009
2009
Earlier work this paper cites.
S. Ross, G. Gordon, and D. Bagnell, “A reduction of imitation learning and structured prediction to no-regret online learning,” in AISTATS , 2011, pp. 627–635
2011
Earlier work this paper cites.
Y. Jiang et al. , “Efficient grasping from rgbd images: Learning using a new rectangle representation,” in ICRA , 2011, pp. 3304–3311
2011
Earlier work this paper cites.
M. Zhu et al. , “Single image 3d object detection and pose estimation for grasping,” in ICRA , 2014, pp. 3936–3943
2014
Earlier work this paper cites.
J. Redmon and A. Angelova, “Real-time grasp detection using convolutional neural networks,” in ICRA , 2015, pp. 1316–1322
2015
Earlier work this paper cites.
I. Lenz et al. , “Deep learning for detecting robotic grasps,” IJRR , vol. 34, no. 4-5, pp. 705–724, 2015
2015
Earlier work this paper cites.
Y. Zhu et al. , “Understanding tools: Task-oriented object modeling, learning and recognition,” in CVPR , 2015, pp. 2855–2864
2015
Earlier work this paper cites.
M. Savva et al. , “Semantically-enriched 3d models for common-sense knowledge,” in CVPR Workshop , 2015, pp. 24–31
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in CVPR , 2016, pp. 770–778
2016
Earlier work this paper cites.
Y. Huang et al. , “Recent data sets on object manipulation: A survey,” Big Data , vol. 4, no. 4, pp. 197–216, 2016
2016
Earlier work this paper cites.
H. S. Koppula and A. Saxena, “Anticipating human activities using object affordances for reactive robotic response,” TPAMI , vol. 38, no. 1, pp. 14–29, 2016
2016
Earlier work this paper cites.
J. Ho and S. Ermon, “Generative adversarial imitation learning,” NeurIPS , vol. 29, 2016
2016
Earlier work this paper cites.
J. Fu et al. , “One-shot learning of manipulation skills with online dynamics adaptation and neural network priors,” in IROS , 2016, pp. 4019–4026
2016
Earlier work this paper cites.
M. Machin et al. , “Smof: A safety monitoring framework for autonomous systems,” TSMC , vol. 48, no. 5, pp. 702–715, 2016
2016
Earlier work this paper cites.
G. Brockman et al. , “Openai gym,” arXiv:1606.01540 , 2016
2016
Earlier work this paper cites.
A. Vaswani et al. , “Attention is all you need,” NeurIPS , vol. 30, 2017
2017
Earlier work this paper cites.
N. Yamanobe, W. Wan et al. , “A brief review of affordance in robotic manipulation research,” Advanced Robotics , vol. 31, no. 19-20, pp. 1086–1101, 2017
2017
Earlier work this paper cites.
S. Kumra and C. Kanan, “Robotic grasp detection using deep convolutional neural networks,” in IROS , 2017, pp. 769–776
2017
Earlier work this paper cites.
J. Varley, C. DeChant, A. Richardson, J. Ruales, and P. Allen, “Shape completion enabled robotic grasping,” in IROS , 2017, pp. 2442–2447
2017
Earlier work this paper cites.
C. R. Qi et al. , “Pointnet: Deep learning on point sets for 3d classification and segmentation,” in CVPR , 2017, pp. 652–660
2017
Earlier work this paper cites.
W. Yuan et al. , “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.
R. Calandra et al. , “The feeling of success: Does touch sensing help predict grasp outcomes?” in CoRL , 2017, pp. 314–323
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
J. Schulman et al. , “Proximal policy optimization algorithms,” arXiv:1707.06347 , 2017
2017
Earlier work this paper cites.
U. Asif et al. , “Rgb-d object recognition and grasp detection using hierarchical cascaded forests,” TRO , vol. 33, no. 3, pp. 547–564, 2017
2017
Earlier work this paper cites.
E. Jang et al. , “End-to-end learning of semantic grasping,” in CoRL , 2017, pp. 119–132
2017
Earlier work this paper cites.
B. Calli et al. , “Yale-cmu-berkeley dataset for robotic manipulation research,” IJRR , vol. 36, no. 3, pp. 261–268, 2017
2017
Earlier work this paper cites.
S. Krishnan et al. , “Transition state clustering: Unsupervised surgical trajectory segmentation for robot learning,” IJRR , vol. 36, no. 13-14, pp. 1595–1618, 2017
2017
Earlier work this paper cites.
L. Jin et al. , “Robot manipulator control using neural networks: A survey,” Neurocomputing , vol. 285, pp. 23–34, 2018
2018
Earlier work this paper cites.
B. Tekin et al. , “Real-time seamless single shot 6d object pose prediction,” in CVPR , 2018, pp. 292–301
2018
Earlier work this paper cites.
M. Schwarz, A. Milan, A. S. Periyasamy, and S. Behnke, “Rgb-d object detection and semantic segmentation for autonomous manipulation in clutter,” IJRR , vol. 37, no. 4-5, pp. 437–451, 2018
2018
Earlier work this paper cites.
T.-H. Pham et al. , “Hand-object contact force estimation from markerless visual tracking,” TPAMI , vol. 40, no. 12, pp. 2883–2896, 2018
2018
Earlier work this paper cites.
R. Calandra et al. , “More than a feeling: Learning to grasp and regrasp using vision and touch,” RAL , vol. 3, no. 4, pp. 3300–3307, 2018
2018
Earlier work this paper cites.
A. Tejani, R. Kouskouridas et al. , “Latent-class hough forests for 6 dof object pose estimation,” TPAMI , vol. 40, no. 1, pp. 119–132, 2018
2018
Earlier work this paper cites.
T.-T. Do et al. , “Affordancenet: An end-to-end deep learning approach for object affordance detection,” in ICRA , 2018, pp. 5882–5889
2018
Earlier work this paper cites.
D. Kalashnikov et al. , “Scalable deep reinforcement learning for vision-based robotic manipulation,” in CoRL , 2018, pp. 651–673
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
F. Torabi et al. , “Behavioral cloning from observation,” arXiv:1805.01954 , 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
W. Wang, R. Li, Z. M. Diekel, Y. Chen, Z. Zhang, and Y. Jia, “Controlling object hand-over in human–robot collaboration via natural wearable sensing,” THMS , vol. 49, no. 1, pp. 59–71, 2018
2018
Earlier work this paper cites.
X. Ye and S. Liu, “Velocity decomposition based planning algorithm for grasping moving object,” in DDCLS , 2018, pp. 644–649
2018
Earlier work this paper cites.
F.-J. Chu et al. , “Real-world multiobject, multigrasp detection,” RAL , vol. 3, no. 4, pp. 3355–3362, 2018
2018
Earlier work this paper cites.
A. Depierre et al. , “Jacquard: A large scale dataset for robotic grasp detection,” in IROS , 2018, pp. 3511–3516
2018
Earlier work this paper cites.
X. Yan et al. , “Learning 6-dof grasping interaction via deep geometry-aware 3d representations,” in ICRA , 2018, pp. 3766–3773
2018
Earlier work this paper cites.
X. B. Peng, M. Andrychowicz et al. , “Sim-to-real transfer of robotic control with dynamics randomization,” in ICRA , 2018, pp. 3803–3810
2018
Earlier work this paper cites.
A. Ajoudani et al. , “Progress and prospects of the human–robot collaboration,” Autonomous Robots , vol. 42, pp. 957–975, 2018
2018
Earlier work this paper cites.
J. Devlin et al. , “Bert: Pre-training of deep bidirectional transformers for language understanding,” in ACL , 2019, pp. 4171–4186
2019
Earlier work this paper cites.
B. Fang et al. , “Survey of imitation learning for robotic manipulation,” IJIRA , vol. 3, pp. 362–369, 2019
2019
Earlier work this paper cites.
A. Billard and D. Kragic, “Trends and challenges in robot manipulation,” Science , vol. 364, no. 6446, p. eaat8414, 2019
2019
Earlier work this paper cites.
H. Liang et al. , “Pointnetgpd: Detecting grasp configurations from point sets,” in ICRA , 2019, pp. 3629–3635
2019
Earlier work this paper cites.
B. Yang, S. Rosa et al. , “Dense 3d object reconstruction from a single depth view,” TPAMI , vol. 41, no. 12, pp. 2820–2834, 2019
2019
Earlier work this paper cites.
Z. Li, G. Wang, and X. Ji, “Cdpn: Coordinates-based disentangled pose network for real-time rgb-based 6-dof object pose estimation,” in ICCV , 2019, pp. 7678–7687
2019
Earlier work this paper cites.
C. Wang et al. , “Densefusion: 6d object pose estimation by iterative dense fusion,” in CVPR , 2019, pp. 3343–3352
2019
Earlier work this paper cites.
H. Wang et al. , “Normalized object coordinate space for category-level 6d object pose and size estimation,” in CVPR , 2019, pp. 2642–2651
2019
Earlier work this paper cites.
T. Nagarajan et al. , “Grounded human-object interaction hotspots from video,” in ICCV , 2019, pp. 8688–8697
2019
Earlier work this paper cites.
N. Marturi et al. , “Dynamic grasp and trajectory planning for moving objects,” Autonomous Robots , vol. 43, pp. 1241–1256, 2019
2019
Earlier work this paper cites.
M. Kiatos and S. Malassiotis, “Robust object grasping in clutter via singulation,” in ICRA , 2019, pp. 1596–1600
2019
Earlier work this paper cites.
B. Sundaralingam and T. Hermans, “Relaxed-rigidity constraints: kinematic trajectory optimization and collision avoidance for in-grasp manipulation,” Autonomous Robots , vol. 43, pp. 469–483, 2019
2019
Earlier work this paper cites.
M. Kyrarini, M. A. Haseeb, D. Ristić-Durrant, and A. Gräser, “Robot learning of industrial assembly task via human demonstrations,” Autonomous Robots , vol. 43, pp. 239–257, 2019
2019
Earlier work this paper cites.
X. Li, Z. Serlin, G. Yang, and C. Belta, “A formal methods approach to interpretable reinforcement learning for robotic planning,” Science Robotics , vol. 4, no. 37, p. eaay6276, 2019
2019
Earlier work this paper cites.
A. Gupta et al. , “Lvis: A dataset for large vocabulary instance segmentation,” in CVPR , 2019, pp. 5356–5364
2019
Earlier work this paper cites.
A. Dosovitskiy et al. , “An image is worth 16x16 words: Transformers for image recognition at scale,” in ICLR , 2020
2020
Earlier work this paper cites.
J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” NeurIPS , vol. 33, pp. 6840–6851, 2020
2020
Earlier work this paper cites.
K. Kleeberger et al. , “A survey on learning-based robotic grasping,” Current Robotics Reports , vol. 1, pp. 239–249, 2020
2020
Earlier work this paper cites.
M. Lambeta et al. , “Digit: A novel design for a low-cost compact high-resolution tactile sensor with application to in-hand manipulation,” RAL , vol. 5, no. 3, pp. 3838–3845, 2020
2020
Earlier work this paper cites.
M. A. Lee, Y. Zhu, P. Zachares, M. Tan et al. , “Making sense of vision and touch: Learning multimodal representations for contact-rich tasks,” TRO , vol. 36, no. 3, pp. 582–596, 2020
2020
Earlier work this paper cites.
X. Li et al. , “Category-level articulated object pose estimation,” in CVPR , 2020, pp. 3706–3715
2020
Earlier work this paper cites.
F. Xiang et al. , “Sapien: A simulated part-based interactive environment,” in CVPR , 2020, pp. 11 097–11 107
2020
Earlier work this paper cites.
R. Boney et al. , “Regularizing model-based planning with energy-based models,” in CoRL , 2020, pp. 182–191
2020
Earlier work this paper cites.
Y. Yue et al. , “Implicit distributional reinforcement learning,” NeurIPS , vol. 33, pp. 7135–7147, 2020
2020
Earlier work this paper cites.
M. Liu, T. He, M. Xu, and W. Zhang, “Energy-based imitation learning,” arXiv:2004.09395 , 2020
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
O. M. Andrychowicz et al. , “Learning dexterous in-hand manipulation,” IJRR , vol. 39, no. 1, pp. 3–20, 2020
2020
Earlier work this paper cites.
A. Murali et al. , “6-dof grasping for target-driven object manipulation in clutter,” in ICRA , 2020, pp. 6232–6238
2020
Earlier work this paper cites.
Y. Yang et al. , “A deep learning approach to grasping the invisible,” RAL , vol. 5, no. 2, pp. 2232–2239, 2020
2020
Earlier work this paper cites.
L. Berscheid et al. , “Self-supervised learning for precise pick-and-place without object model,” RAL , vol. 5, no. 3, pp. 4828–4835, 2020
2020
Earlier work this paper cites.
K. Zakka et al. , “Form2fit: Learning shape priors for generalizable assembly from disassembly,” in ICRA , 2020, pp. 9404–9410
2020
Earlier work this paper cites.
A. Nagabandi et al. , “Deep dynamics models for learning dexterous manipulation,” in CoRL , 2020, pp. 1101–1112
2020
Earlier work this paper cites.
Z. Qin et al. , “Keto: Learning keypoint representations for tool manipulation,” in ICRA , 2020, pp. 7278–7285
2020
Earlier work this paper cites.
K. Fang, Y. Zhu, A. Garg et al. , “Learning task-oriented grasping for tool manipulation from simulated self-supervision,” IJRR , vol. 39, no. 2-3, pp. 202–216, 2020
2020
Earlier work this paper cites.
D. Morrison et al. , “Learning robust, real-time, reactive robotic grasping,” IJRR , vol. 39, no. 2-3, pp. 183–201, 2020
2020
Earlier work this paper cites.
D. Morrison et al. , “Egad! an evolved grasping analysis dataset for diversity and reproducibility in robotic manipulation,” RAL , vol. 5, no. 3, pp. 4368–4375, 2020
2020
Earlier work this paper cites.
H.-S. Fang et al. , “Graspnet-1billion: A large-scale benchmark for general object grasping,” in CVPR , 2020, pp. 11 444–11 453
2020
Earlier work this paper cites.
K. Onur, O. T. Kaymakci, and M. Mercimek, “Advanced predictive maintenance with machine learning failure estimation in industrial packaging robots,” in DAS , 2020, pp. 1–6
2020
Earlier work this paper cites.
J. Oyekan et al. , “Applying a 6 dof robotic arm and digital twin to automate fan-blade reconditioning for aerospace maintenance, repair, and overhaul,” Sensors , vol. 20, no. 16, p. 4637, 2020
2020
Earlier work this paper cites.
B. Zhong and L. Xia, “A systematic review on exploring the potential of educational robotics in mathematics education,” Int J Sci Math Educ , vol. 18, no. 1, pp. 79–101, 2020
2020
Earlier work this paper cites.
S. James et al. , “Rlbench: The robot learning benchmark & learning environment,” RAL , vol. 5, no. 2, pp. 3019–3026, 2020
2020
Earlier work this paper cites.
T.-W. Chin et al. , “Towards efficient model compression via learned global ranking,” in CVPR , 2020, pp. 1518–1528
2020
Earlier work this paper cites.
A. Gupta et al. , “Embodied intelligence via learning and evolution,” Nature Communications , vol. 12, no. 1, p. 5721, 2021
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
B. Mildenhall, P. P. Srinivasan et al. , “Nerf: Representing scenes as neural radiance fields for view synthesis,” Communications of the ACM , vol. 65, no. 1, pp. 99–106, 2021
2021
Earlier work this paper cites.
G. Du, K. Wang, S. Lian, and K. Zhao, “Vision-based robotic grasping from object localization, object pose estimation to grasp estimation for parallel grippers: a review,” Artificial Intelligence Review , vol. 54, no. 3, pp. 1677–1734, 2021
2021
Earlier work this paper cites.
Y. Cong et al. , “A comprehensive study of 3-d vision-based robot manipulation,” TCYB , vol. 53, no. 3, pp. 1682–1698, 2021
2021
Earlier work this paper cites.
O. Kroemer, S. Niekum, and G. Konidaris, “A review of robot learning for manipulation: Challenges, representations, and algorithms,” JMLR , vol. 22, no. 30, pp. 1–82, 2021
2021
Cited alongside, same era.
2021
Cited alongside, same era.
J. Cui and J. Trinkle, “Toward next-generation learned robot manipulation,” Science Robotics , vol. 6, no. 54, p. eabd9461, 2021
2021
Cited alongside, same era.
D. De Gregorio, R. Zanella, G. Palli, and L. Di Stefano, “Effective deployment of cnns for 3dof pose estimation and grasping in industrial settings,” in ICPR , 2021, pp. 7419–7426
2021
Cited alongside, same era.
S. Iwase et al. , “Repose: Fast 6d object pose refinement via deep texture rendering,” in ICCV , 2021, pp. 3303–3312
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
A. Simeonov et al. , “Se (3)-equivariant relational rearrangement with neural descriptor fields,” in CoRL , 2023, pp. 835–846
2023
Later among the works it cites.
E. Chun, Y. Du, A. Simeonov, T. Lozano-Perez, and L. Kaelbling, “Local neural descriptor fields: Locally conditioned object representations for manipulation,” in ICRA , 2023, pp. 1830–1836
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2021
Cited alongside, same era.
K. Chen and Q. Dou, “Sgpa: Structure-guided prior adaptation for category-level 6d object pose estimation,” in ICCV , 2021, pp. 2773–2782
2021
Cited alongside, same era.
K. Mo, L. J. Guibas, M. Mukadam et al. , “Where2act: From pixels to actions for articulated 3d objects,” in ICCV , 2021, pp. 6813–6823
2021
Cited alongside, same era.
A. Mandlekar et al. , “What matters in learning from offline human demonstrations for robot manipulation,” in CoRL , 2021
2021
Cited alongside, same era.
2021
Cited alongside, same era.
H. Kim et al. , “Transformer-based deep imitation learning for dual-arm robot manipulation,” in IROS , 2021, pp. 8965–8972
2021
Cited alongside, same era.
M. Sundermeyer et al. , “Contact-graspnet: Efficient 6-dof grasp generation in cluttered scenes,” in ICRA , 2021, pp. 13 438–13 444
2021
Cited alongside, same era.
J. Lundell et al. , “Ddgc: Generative deep dexterous grasping in clutter,” RAL , vol. 6, no. 4, pp. 6899–6906, 2021
2021
Cited alongside, same era.
2023
Later among the works it cites.
2023
Later among the works it cites.
J. Urain et al. , “Se (3)-diffusionfields: Learning smooth cost functions for joint grasp and motion optimization through diffusion,” in ICRA , 2023, pp. 5923–5930
2023
Later among the works it cites.
Y. Xu et al. , “Unidexgrasp: Universal robotic dexterous grasping via learning diverse proposal generation and goal-conditioned policy,” in CVPR , 2023, pp. 4737–4746
2023
Later among the works it cites.
W. Wan et al. , “Unidexgrasp++: Improving dexterous grasping policy learning via geometry-aware curriculum and iterative generalist-specialist learning,” in ICCV , 2023, pp. 3891–3902
2023
Later among the works it cites.
2023
Later among the works it cites.
T. Zhang, S. Guo et al. , “Adjacency constraint for efficient hierarchical reinforcement learning,” TPAMI , vol. 45, no. 4, pp. 4152–4166, 2023
2023
Later among the works it cites.
R. Gong, J. Huang, Y. Zhao, H. Geng et al. , “Arnold: A benchmark for language-grounded task learning with continuous states in realistic 3d scenes,” in ICCV , 2023, pp. 20 483–20 495
2023
Later among the works it cites.
D.-H. Zhai et al. , “Fanet: Fast and accurate robotic grasp detection based on keypoints,” TASE , 2023
2023
Later among the works it cites.
H.-S. Fang et al. , “Anygrasp: Robust and efficient grasp perception in spatial and temporal domains,” TRO , 2023
2023
Later among the works it cites.
Q. Dai et al. , “Graspnerf: Multiview-based 6-dof grasp detection for transparent and specular objects using generalizable nerf,” in ICRA , 2023, pp. 1757–1763
2023
Later among the works it cites.
J. Kim et al. , “Transpose: Large-scale multispectral dataset for transparent object,” IJRR , p. 02783649231213117, 2023
2023
Later among the works it cites.
H. Yu et al. , “Tgf-net: Sim2real transparent object 6d pose estimation based on geometric fusion,” RAL , vol. 8, no. 6, pp. 3868–3875, 2023
2023
Later among the works it cites.
B. Wei et al. , “Discriminative active learning for robotic grasping in cluttered scene,” RAL , vol. 8, no. 3, pp. 1858–1865, 2023
2023
Later among the works it cites.
K. Xu et al. , “A joint modeling of vision-language-action for target-oriented grasping in clutter,” in ICRA , 2023, pp. 11 597–11 604
2023
Later among the works it cites.
G. Zhang et al. , “Flexible handover with real-time robust dynamic grasp trajectory generation,” in IROS , 2023, pp. 3192–3199
2023
Later among the works it cites.
J. Liu et al. , “Target-referenced reactive grasping for dynamic objects,” in CVPR , 2023, pp. 8824–8833
2023
Later among the works it cites.
W. C. Agboh et al. , “Learning to efficiently plan robust frictional multi-object grasps,” in IROS , 2023, pp. 10 660–10 667
2023
Later among the works it cites.
S. Aeron et al. , “Push-mog: Efficient pushing to consolidate polygonal objects for multi-object grasping,” in CASE , 2023, pp. 1–6
2023
Later among the works it cites.
K. Yao and A. Billard, “Exploiting kinematic redundancy for robotic grasping of multiple objects,” TRO , vol. 39, no. 3, pp. 1982–2002, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
H. Geng et al. , “Gapartnet: Cross-category domain-generalizable object perception and manipulation via generalizable and actionable parts,” in CVPR , 2023, pp. 7081–7091
2023
Later among the works it cites.
H. Geng et al. , “Partmanip: Learning cross-category generalizable part manipulation policy from point cloud observations,” in CVPR , 2023, pp. 2978–2988
2023
Later among the works it cites.
S. P. Arunachalam et al. , “Dexterous imitation made easy: A learning-based framework for efficient dexterous manipulation,” in ICRA , 2023, pp. 5954–5961
2023
Later among the works it cites.
S. Li et al. , “Dexdeform: Dexterous deformable object manipulation with human demonstrations and differentiable physics,” in ICLR , 2023
2023
Later among the works it cites.
A. Z. Ren et al. , “Leveraging language for accelerated learning of tool manipulation,” in CoRL , 2023, pp. 1531–1541
2023
Later among the works it cites.
M. Xu et al. , “Creative robot tool use with large language models,” arXiv:2310.13065 , 2023
2023
Later among the works it cites.
H. Sun, Z. Zhang, H. Wang, Y. Wang, and Q. Cao, “A novel robotic grasp detection framework using low-cost rgb-d camera for industrial bin picking,” TIM , vol. 73, pp. 1–12, 2023
2023
Later among the works it cites.
S. Ge et al. , “Pixel-level collision-free grasp prediction network for medical test tube sorting on cluttered trays,” RAL , vol. 8, no. 12, pp. 7897–7904, 2023
2023
Later among the works it cites.
S. D’Avella et al. , “The cluttered environment picking benchmark (cepb) for advanced warehouse automation: Evaluating the perception, planning, control, and grasping of manipulation systems,” RAM , 2023
2023
Later among the works it cites.
J. Jiang et al. , “Robotic perception of transparent objects: A review,” TAI , vol. 1, no. 01, pp. 1–21, 2023
2023
Later among the works it cites.
T. Zhu, R. Wu, J. Hang, X. Lin, and Y. Sun, “Toward human-like grasp: Functional grasp by dexterous robotic hand via object-hand semantic representation,” TPAMI , vol. 45, no. 10, pp. 12 521–12 534, 2023
2023
Later among the works it cites.
M. Qin et al. , “Robot tool use: A survey,” Frontiers in Robotics and AI , vol. 9, p. 1009488, 2023
2023
Later among the works it cites.
Y. Shirai, D. K. Jha, A. U. Raghunathan, and D. Hong, “Tactile tool manipulation,” in ICRA , 2023, pp. 12 597–12 603
2023
Later among the works it cites.
2023
Later among the works it cites.
J. Gu, F. Xiang, X. Li, Z. Ling et al. , “Maniskill2: A unified benchmark for generalizable manipulation skills,” ICLR , 2023
2023
Later among the works it cites.
Y. Chen, Y. Geng, F. Zhong et al. , “Bi-dexhands: Towards human-level bimanual dexterous manipulation,” TPAMI , no. 01, pp. 1–15, 2023
2023
Later among the works it cites.
C. Bao et al. , “Dexart: Benchmarking generalizable dexterous manipulation with articulated objects,” in CVPR , 2023, pp. 21 190–21 200
2023
Later among the works it cites.
Y. Long et al. , “Human-in-the-loop embodied intelligence with interactive simulation environment for surgical robot learning,” RAL , 2023
2023
Later among the works it cites.
X. Wang et al. , “Robust adaptive learning control of space robot for target capturing using neural network,” TNNLS , vol. 34, no. 10, pp. 7567–7577, 2023
2023
Later among the works it cites.
D. Driess, F. Xia, M. S. Sajjadi et al. , “Palm-e: An embodied multimodal language model,” in ICML , 2023, pp. 8469–8488
2023
Later among the works it cites.
2023
Later among the works it cites.
J. Xu et al. , “Reasoning tuning grasp: Adapting multi-modal large language models for robotic grasping,” in CoRL Workshop , 2023
2023
Later among the works it cites.
Z. Jin et al. , “A learning based hierarchical control framework for human–robot collaboration,” TASE , vol. 20, no. 1, pp. 506–517, 2023
2023
Later among the works it cites.
B. Zitkovich et al. , “Rt-2: Vision-language-action models transfer web knowledge to robotic control,” in CoRL , 2023, pp. 2165–2183
2023
Later among the works it cites.
T.-H. Wang et al. , “Measuring interpretability of neural policies of robots with disentangled representation,” in CoRL , 2023, pp. 602–641
2023
Later among the works it cites.
J. Ji et al. , “Safety gymnasium: A unified safe reinforcement learning benchmark,” NeurIPS , vol. 36, 2023
2023
Later among the works it cites.
C. Chen, C. Liu, T. Wang, A. Zhang, W. Wu, and L. Cheng, “Compound fault diagnosis for industrial robots based on dual-transformer networks,” J Manuf Syst , vol. 66, pp. 163–178, 2023
2023
Later among the works it cites.
2024
Closest in time.
2024
Closest in time.
Y. Ma et al. , “A survey on vision-language-action models for embodied ai,” arXiv:2405.14093 , 2024
2024
Closest in time.
2024
Closest in time.
A. Agrawal et al. , “Clear-splatting: Learning residual gaussian splats for transparent object manipulation,” in ICRA Workshop , 2024
2024
Closest in time.
2024
Closest in time.
Y. Li and D. Pathak, “Object-aware gaussian splatting for robotic manipulation,” in ICRA Workshop , 2024
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
L. Xu et al. , “6d-diff: A keypoint diffusion framework for 6d object pose estimation,” in CVPR , 2024, pp. 9676–9686
2024
Closest in time.
Z. Dang, L. Wang, Y. Guo, and M. Salzmann, “Match normalization: Learning-based point cloud registration for 6d object pose estimation in the real world,” TPAMI , 2024
2024
Closest in time.
V. N. Nguyen et al. , “Gigapose: Fast and robust novel object pose estimation via one correspondence,” in CVPR , 2024, pp. 9903–9913
2024
Closest in time.
J. Lin et al. , “Sam-6d: Segment anything model meets zero-shot 6d object pose estimation,” in CVPR , 2024, pp. 27 906–27 916
2024
Closest in time.
B. Wen et al. , “Foundationpose: Unified 6d pose estimation and tracking of novel objects,” in CVPR , 2024, pp. 17 868–17 879
2024
Closest in time.
Y. Li, N. Zhao, J. Xiao et al. , “Laso: Language-guided affordance segmentation on 3d object,” in CVPR , 2024, pp. 14 251–14 260
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
C. Ning et al. , “Where2explore: Few-shot affordance learning for unseen novel categories of articulated objects,” NeurIPS , vol. 36, 2024
2024
Closest in time.
2024
Closest in time.
R. Wu et al. , “Learning environment-aware affordance for 3d articulated object manipulation under occlusions,” NeurIPS , vol. 36, 2024
2024
Closest in time.
Y. Liang, K. Ellis, and J. Henriques, “Rapid motor adaptation for robotic manipulator arms,” in CVPR , 2024, pp. 16 404–16 413
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
M. Reuss et al. , “Multimodal diffusion transformer: Learning versatile behavior from multimodal goals,” in ICRA Workshop , 2024
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
J. Zeng et al. , “Learning manipulation by predicting interaction,” arXiv:2406.00439 , 2024
2024
Closest in time.
J. Brehmer et al. , “Edgi: Equivariant diffusion for planning with embodied agents,” NeurIPS , vol. 36, 2024
2024
Closest in time.
A. Szot et al. , “Grounding multimodal large language models in actions,” arXiv:2406.07904 , 2024
2024
Closest in time.
M. J. Kim et al. , “Openvla: An open-source vision-language-action model,” arXiv:2406.09246 , 2024
2024
Closest in time.
H. Zhen et al. , “3d-vla: 3d vision-language-action generative world model,” arXiv:2403.09631 , 2024
2024
Closest in time.
S. Hu, L. Shen et al. , “On transforming reinforcement learning with transformers: The development trajectory,” TPAMI , 2024
2024
Closest in time.
Y. Li et al. , “Grasp multiple objects with one hand,” RAL , 2024
2024
Closest in time.
2024
Closest in time.
J. Lee et al. , “Nfl: Normal field learning for 6-dof grasping of transparent objects,” RAL , 2024
2024
Closest in time.
2024
Closest in time.
T. Chen and Y. Sun, “Multi-object grasping–experience forest for robotic finger movement strategies,” RAL , 2024
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
E. Aljalbout, F. Frank, M. Karl et al. , “On the role of the action space in robot manipulation learning and sim-to-real transfer,” RAL , vol. 9, no. 6, pp. 5895–5902, 2024
2024
Closest in time.
2024
Closest in time.
H. Ma, M. Shi et al. , “Generalizing 6-dof grasp detection via domain prior knowledge,” in CVPR , 2024, pp. 18 102–18 111
2024
Closest in time.
2024
Closest in time.
Y. Shvets et al. , “Robotics in agriculture: Advanced technologies in livestock farming and crop cultivation,” in E3S Web Conf. , vol. 480, 2024, p. 03024
2024
Closest in time.
K. Kawaharazuka et al. , “Continuous object state recognition for cooking robots using pre-trained vision-language models and black-box optimization,” RAL , 2024
2024
Closest in time.