Fetching the paper…
Reading the bibliography…
We present RoboGen, a generative robotic agent that automatically learns diverse robotic skills at scale via generative simulation.
Bleu: a method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., and Zhu, W.-J · 2002
Earlier work this paper cites.
Sampling-based algorithms for optimal motion planning
Karaman, S. and Frazzoli, E · 2011
Earlier work this paper cites.
The open motion planning library
Sucan, I. A., Moll, M., and Kavraki, L. E · 2012
Earlier work this paper cites.
Mujoco: A physics engine for model-based control
Todorov, E., Erez, T., and Tassa, Y · 2012
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
Earlier work this paper cites.
Unified particle physics for real-time applications
Macklin, M., Müller, M., Chentanez, N., and Kim, T.-Y · 2014
Earlier work this paper cites.
Batch informed trees (bit*): Sampling-based optimal planning via the heuristically guided search of implicit random geometric graphs
Gammell, J. D., Srinivasa, S. S., and Barfoot, T. D · 2015
Earlier work this paper cites.
Pybullet, a python module for physics simulation for games, robotics and machine learning
Coumans, E. and Bai, Y · 2016
Earlier work this paper cites.
Proximal policy optimization algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O · 2017
Earlier work this paper cites.
Domain randomization for transferring deep neural networks from simulation to the real world
Tobin, J., Fong, R., Ray, A., Schneider, J., Zaremba, W., and Abbeel, P · 2017
Earlier work this paper cites.
Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Haarnoja, T., Zhou, A., Abbeel, P., and Levine, S · 2018
Earlier work this paper cites.
Texygen: A benchmarking platform for text generation models
Zhu, Y., Lu, S., Zheng, L., Guo, J., Zhang, W., Wang, J., and Yu, Y · 2018
Earlier work this paper cites.
Solving rubik’s cube with a robot hand
Akkaya, I., Andrychowicz, M., Chociej, M., Litwin, M., McGrew, B., Petron, A., Paino, A., Plappert, M., Powell, G., Ribas, R., et al · 2019
Earlier work this paper cites.
Sentence-bert: Sentence embeddings using siamese bert-networks
Reimers, N. and Gurevych, I · 2019
Earlier work this paper cites.
An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al · 2020
Earlier work this paper cites.
Rlbench: The robot learning benchmark & learning environment
James, S., Ma, Z., Arrojo, D. R., and Davison, A. J · 2020
Earlier work this paper cites.
Kaufmann, E., Loquercio, A., Ranftl, R., Müller, M., Koltun, V., and Scaramuzza, D · 2020
Earlier work this paper cites.
Incremental potential contact: intersection-and inversion-free, large-deformation dynamics
Li, M., Ferguson, Z., Schneider, T., Langlois, T. R., Zorin, D., Panozzo, D., Jiang, C., and Kaufman, D. M · 2020
Earlier work this paper cites.
Softgym: Benchmarking deep reinforcement learning for deformable object manipulation
Lin, X., Wang, Y., Olkin, J., and Held, D · 2020
Earlier work this paper cites.
Sapien: A simulated part-based interactive environment
Xiang, F., Qin, Y., Mo, K., Xia, Y., Zhu, H., Liu, F., Liu, M., Jiang, H., Yuan, Y., Wang, H., et al · 2020
Earlier work this paper cites.
Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning
Yu, T., Quillen, D., He, Z., Julian, R., Hausman, K., Finn, C., and Levine, S · 2020
Earlier work this paper cites.
Tossingbot: Learning to throw arbitrary objects with residual physics
Zeng, A., Song, S., Lee, J., Rodriguez, A., and Funkhouser, T · 2020
Earlier work this paper cites.
Threedworld: A platform for interactive multi-modal physical simulation, 2021
Gan, C., Schwartz, J., Alter, S., Mrowca, D., Schrimpf, M., Traer, J., Freitas, J. D., Kubilius, J., Bhandwaldar, A., Haber, N., Sano, M., Kim, K., Wang, E., Lingelbach, M., Curtis, A., Feigelis, K., Bear, D. M., Gutfreund, D., Cox, D., Torralba, A., DiCarlo, J. J., Tenenbaum, J. B., McDermott, J. H., and Yamins, D. L. K · 2021
Earlier work this paper cites.
Disect: A differentiable simulation engine for autonomous robotic cutting
Heiden, E., Macklin, M., Narang, Y., Fox, D., Garg, A., and Ramos, F · 2021
Earlier work this paper cites.
The role of physics-based simulators in robotics
Liu, C. K. and Negrut, D · 2021
Earlier work this paper cites.
Learning high-speed flight in the wild
Loquercio, A., Kaufmann, E., Ranftl, R., Müller, M., Koltun, V., and Scaramuzza, D · 2021
Cited alongside, same era.
Guided imitation of task and motion planning, 2021
McDonald, M. J. and Hadfield-Menell, D · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al · 2021
Cited alongside, same era.
Deep marching tetrahedra: a hybrid representation for high-resolution 3d shape synthesis
Shen, T., Gao, J., Yin, K., Liu, M.-Y., and Fidler, S · 2021
Cited alongside, same era.
Do as i can, not as i say: Grounding language in robotic affordances
Ahn, M., Brohan, A., Brown, N., Chebotar, Y., Cortes, O., David, B., Finn, C., Gopalakrishnan, K., Hausman, K., Herzog, A., et al · 2022
Cited alongside, same era.
Maniskill2: A unified benchmark for generalizable manipulation skills
Gu, J., Xiang, F., Li, X., Ling, Z., Liu, X., Mu, T., Tang, Y., Tao, S., Wei, X., Yao, Y., et al · 2023
Closest in time.
Scaling up and distilling down: Language-guided robot skill acquisition
Ha, H., Florence, P., and Song, S · 2023
Closest in time.
Learning agile soccer skills for a bipedal robot with deep reinforcement learning
Haarnoja, T., Moran, B., Lever, G., Huang, S. H., Tirumala, D., Wulfmeier, M., Humplik, J., Tunyasuvunakool, S., Siegel, N. Y., Hafner, R., et al · 2023
Closest in time.
Voxposer: Composable 3d value maps for robotic manipulation with language models
Huang, W., Wang, C., Zhang, R., Li, Y., Wu, J., and Fei-Fei, L · 2023
Closest in time.
Vima: Robot manipulation with multimodal prompts
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A system for general in-hand object re-orientation
Chen, T., Xu, J., and Agrawal, P · 2022
Cited alongside, same era.
Objaverse: A universe of annotated 3d objects
Deitke, M., Schwenk, D., Salvador, J., Weihs, L., Michel, O., VanderBilt, E., Schmidt, L., Ehsani, K., Kembhavi, A., and Farhadi, A · 2022
Cited alongside, same era.
Inner monologue: Embodied reasoning through planning with language models
Huang, W., Xia, F., Xiao, T., Chan, H., Liang, J., Florence, P., Zeng, A., Tompson, J., Mordatch, I., Chebotar, Y., et al · 2022
Cited alongside, same era.
Code as policies: Language model programs for embodied control
Liang, J., Huang, W., Xia, F., Xu, P., Hausman, K., Ichter, B., Florence, P., and Zeng, A · 2022
Cited alongside, same era.
Lin, X., Huang, Z., Li, Y., Tenenbaum, J. B., Held, D., and Gan, C · 2022
Cited alongside, same era.
Midjourney
Midjourney · 2022
Cited alongside, same era.
Dreamfusion: Text-to-3d using 2d diffusion
Poole, B., Jain, A., Barron, J. T., and Mildenhall, B · 2022
Cited alongside, same era.
Jiang, Y., Gupta, A., Zhang, Z., Wang, G., Dou, Y., Chen, Y., Fei-Fei, L., Anandkumar, A., Zhu, Y., and Fan, L · 2023
Closest in time.
Scaling up gans for text-to-image synthesis
Kang, M., Zhu, J.-Y., Zhang, R., Park, J., Shechtman, E., Paris, S., and Park, T · 2023
Closest in time.
Dall-e-bot: Introducing web-scale diffusion models to robotics
Kapelyukh, I., Vosylius, V., and Johns, E · 2023
Closest in time.
Gen2sim: Scaling up robot learning in simulation with generative models
Katara, P., Xian, Z., and Fragkiadaki, K · 2023
Closest in time.
Text2motion: From natural language instructions to feasible plans
Lin, K., Agia, C., Migimatsu, T., Pavone, M., and Bohg, J · 2023
Closest in time.
Scalable 3d captioning with pretrained models
Luo, T., Rockwell, C., Lee, H., and Johnson, J · 2023
Closest in time.
Eureka: Human-level reward design via coding large language models
Ma, Y. J., Liang, W., Wang, G., Huang, D.-A., Bastani, O., Jayaraman, D., Zhu, Y., Fan, L., and Anandkumar, A · 2023
Closest in time.
Realfusion: 360 { \{ \ \backslash deg } \} reconstruction of any object from a single image
Melas-Kyriazi, L., Rupprecht, C., Laina, I., and Vedaldi, A · 2023
Closest in time.
Cabinet: Scaling neural collision detection for object rearrangement with procedural scene generation, 2023
Murali, A., Mousavian, A., Eppner, C., Fishman, A., and Fox, D · 2023
Closest in time.
OpenAI · 2023
Closest in time.
Learning humanoid locomotion with transformers
Radosavovic, I., Xiao, T., Zhang, B., Darrell, T., Malik, J., and Sreenath, K · 2023
Closest in time.
Toolflownet: Robotic manipulation with tools via predicting tool flow from point clouds
Seita, D., Wang, Y., Shetty, S. J., Li, E. Y., Erickson, Z., and Held, D · 2023
Closest in time.
Reaching the limit in autonomous racing: Optimal control versus reinforcement learning
Song, Y., Romero, A., Müller, M., Koltun, V., and Scaramuzza, D · 2023
Closest in time.
Stanford alpaca: An instruction-following llama model
Taori, R., Gulrajani, I., Zhang, T., Dubois, Y., Li, X., Guestrin, C., Liang, P., and Hashimoto, T. B · 2023
Closest in time.
Gemini: a family of highly capable multimodal models
Team, G., Anil, R., Borgeaud, S., Wu, Y., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., et al · 2023
Closest in time.
Llama: Open and efficient foundation language models
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al · 2023
Closest in time.
Tidybot: Personalized robot assistance with large language models
Wu, J., Antonova, R., Kan, A., Lepert, M., Zeng, A., Song, S., Bohg, J., Rusinkiewicz, S., and Funkhouser, T · 2023
Closest in time.
Roboninja: Learning an adaptive cutting policy for multi-material objects
Xu, Z., Xian, Z., Lin, X., Chi, C., Huang, Z., Gan, C., and Song, S · 2023
Closest in time.
Close the optical sensing domain gap by physics-grounded active stereo sensor simulation
Zhang, X., Chen, R., Li, A., Xiang, F., Qin, Y., Gu, J., Ling, Z., Liu, M., Zeng, P., Han, S., et al · 2023
Closest in time.
Zhuang, Z., Fu, Z., Wang, J., Atkeson, C., Schwertfeger, S., Finn, C., and Zhao, H · 2023
Closest in time.