Fetching the paper…
Reading the bibliography…
We present the task of "Social Rearrangement", consisting of cooperative everyday tasks like setting up the dinner table, tidying a house or unpacking groceries in a simulated multi-agent environment.
Does the chimpanzee have a theory of mind?
Premack, D. and Woodruff, G · 1978
Earlier work this paper cites.
Long short-term memory
Hochreiter, S. and Schmidhuber, J · 1997
Earlier work this paper cites.
Objectnav revisited: On evaluation of embodied agents navigating to objects
Batra, D., Gokaslan, A., Kembhavi, A., Maksymets, O., Mottaghi, R., Savva, M., Toshev, A., and Wijmans, E · 2006
Earlier work this paper cites.
Bridging the imitation gap by adaptive insubordination
Weihs, L., Jain, U., Salvador, J., Lazebnik, S., Kembhavi, A., and Schwing, A · 2007
Earlier work this paper cites.
Allenact: A framework for embodied ai research
Weihs, L., Salvador, J., Kotar, K., Jain, U., Zeng, K.-H., Mottaghi, R., and Kembhavi, A · 2008
Earlier work this paper cites.
Ad hoc autonomous agent teams: Collaboration without pre-coordination
Stone, P., Kaminka, G. A., Kraus, S., and Rosenschein, J. S · 2010
Earlier work this paper cites.
Empirical evaluation of ad hoc teamwork in the pursuit domain
Barrett, S., Stone, P., and Kraus, S · 2011
Earlier work this paper cites.
Rearrangement: A challenge for embodied ai
Batra, D., Chang, A. X., Chernova, S., Davison, A. J., Deng, J., Koltun, V., Levine, S., Malik, J., Mordatch, I., Mottaghi, R., et al · 2011
Earlier work this paper cites.
Submodular function maximization
Krause, A. and Golovin, D · 2014
Earlier work this paper cites.
The ycb object and model set: Towards common benchmarks for manipulation research
Calli, B., Singh, A., Walsman, A., Srinivasa, S., Abbeel, P., and Dollar, A. M · 2015
Earlier work this paper cites.
Robots that can adapt like animals
Cully, A., Clune, J., Tarapore, D., and Mouret, J.-B · 2015
Earlier work this paper cites.
Fictitious self-play in extensive-form games
Heinrich, J., Lanctot, M., and Silver, D · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
Deep reinforcement learning from self-play in imperfect-information games
Heinrich, J. and Silver, D · 2016
Earlier work this paper cites.
Quality diversity: A new frontier for evolutionary computation
Pugh, J. K., Soros, L. B., and Stanley, K. O · 2016
Earlier work this paper cites.
Matterport3d: Learning from rgb-d data in indoor environments
Chang, A., Dai, A., Funkhouser, T., Halber, M., Niessner, M., Savva, M., Song, S., Zeng, A., and Zhang, Y · 2017
Earlier work this paper cites.
Population based training of neural networks
Jaderberg, M., Dalibard, V., Osindero, S., Czarnecki, W. M., Donahue, J., Razavi, A., Vinyals, O., Green, T., Dunning, I., Simonyan, K., et al · 2017
Earlier work this paper cites.
A unified game-theoretic approach to multiagent reinforcement learning
Lanctot, M., Zambaldi, V., Gruslys, A., Lazaridou, A., Tuyls, K., Pérolat, J., Silver, D., and Graepel, T · 2017
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.
Vision-and-language navigation: Interpreting visually-grounded navigation instructions in real environments
Anderson, P., Wu, Q., Teney, D., Bruce, J., Johnson, M., Sünderhauf, N., Reid, I., Gould, S., and van den Hengel, A · 2018
Earlier work this paper cites.
Diversity is all you need: Learning skills without a reward function
Eysenbach, B., Gupta, A., Ibarz, J., and Levine, S · 2018
Earlier work this paper cites.
Unity: A general platform for intelligent agents
Juliani, A., Berges, V.-P., Teng, E., Cohen, A., Harper, J., Elion, C., Goy, C., Gao, Y., Henry, H., Mattar, M., et al · 2018
Earlier work this paper cites.
Virtualhome: Simulating household activities via programs
Puig, X., Ra, K., Boben, M., Li, J., Wang, T., Fidler, S., and Torralba, A · 2018
Earlier work this paper cites.
Gibson env: Real-world perception for embodied agents
Xia, F., Zamir, A. R., He, Z., Sax, A., Malik, J., and Savarese, S · 2018
Cited alongside, same era.
On the utility of learning about humans for human-ai coordination
Carroll, M., Shah, R., Ho, M. K., Griffiths, T., Seshia, S., Abbeel, P., and Dragan, A · 2019
Cited alongside, same era.
Chen, B., Song, S., Lipson, H., and Vondrick, C · 2019
Cited alongside, same era.
On the utility of model learning in hri
Choudhury, R., Swamy, G., Hadfield-Menell, D., and Dragan, A. D · 2019
Cited alongside, same era.
Human-level performance in 3d multiplayer games with population-based reinforcement learning
Jaderberg, M., Czarnecki, W. M., Dunning, I., Marris, L., Lever, G., Castaneda, A. G., Beattie, C., Rabinowitz, N. C., Morcos, A. S., Ruderman, A., et al · 2019
Cited alongside, same era.
Multion: Benchmarking semantic map memory using multi-object navigation
Wani, S., Patel, S., Jain, U., Chang, A., and Savva, M · 2020
Later among the works it 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., Yi, L., Chang, A. X., Guibas, L. J., and Su, H · 2020
Later among the works it cites.
Robustnav: Towards benchmarking robustness in embodied navigation
Chattopadhyay, P., Hoffman, J., Mottaghi, R., and Kembhavi, A · 2021
Later among the works it cites.
Adaptable agent populations via a generative model of policies
Derek, K. and Isola, P · 2021
Later among the works it cites.
Manipulathor: A framework for visual object manipulation
Ehsani, K., Han, W., Herrasti, A., VanderBilt, E., Weihs, L., Kolve, E., Kembhavi, A., and Mottaghi, R · 2021
Later among the works it cites.
Threedworld: A platform for interactive multi-modal physical simulation
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Jain, U., Weihs, L., Kolve, E., Rastegari, M., Lazebnik, S., Farhadi, A., Schwing, A. G., and Kembhavi, A · 2019
Cited alongside, same era.
AI2-THOR: an interactive 3d environment for visual AI
Kolve, E., Mottaghi, R., Han, W., VanderBilt, E., Weihs, L., Herrasti, A., Gordon, D., Zhu, Y., Gupta, A., and Farhadi, A · 2019
Cited alongside, same era.
Maven: Multi-agent variational exploration
Mahajan, A., Rashid, T., Samvelyan, M., and Whiteson, S · 2019
Cited alongside, same era.
Habitat: A Platform for Embodied AI Research
Savva, M., Kadian, A., Maksymets, O., Zhao, Y., Wijmans, E., Jain, B., Straub, J., Liu, J., Koltun, V., Malik, J., Parikh, D., and Batra, D · 2019
Cited alongside, same era.
Dd-ppo: Learning near-perfect pointgoal navigators from 2.5 billion frames
Wijmans, E., Kadian, A., Morcos, A., Lee, S., Essa, I., Parikh, D., Savva, M., and Batra, D · 2019
Cited alongside, same era.
Interactive gibson: A benchmark for interactive navigation in cluttered environments
Xia, F., Shen, W. B., Li, C., Kasimbeg, P., Tchapmi, M., Toshev, A., Martín-Martín, R., and Savarese, S · 2019
Cited alongside, same era.
The hanabi challenge: A new frontier for ai research
Bard, N., Foerster, J. N., Chandar, S., Burch, N., Lanctot, M., Song, H. F., Parisotto, E., Dumoulin, V., Moitra, S., Hughes, E., et al · 2020
Cited alongside, same era.
Gan, C., Schwartz, J., Alter, S., Mrowca, D., Schrimpf, M., Traer, J., De Freitas, J., Kubilius, J., Bhandwaldar, A., Haber, N., et al · 2021
Later among the works it cites.
Off-belief learning
Hu, H., Lerer, A., Cui, B., Pineda, L., Brown, N., and Foerster, J · 2021
Later among the works it cites.
Gridtopix: Training embodied agents with minimal supervision
Jain, U., Liu, I.-J., Lazebnik, S., Kembhavi, A., Weihs, L., and Schwing, A. G · 2021
Later among the works it cites.
Trajectory diversity for zero-shot coordination
Lupu, A., Cui, B., Hu, H., and Foerster, J · 2021
Later among the works it cites.
TEACh: Task-driven embodied agents that chat
Padmakumar, A., Thomason, J., Shrivastava, A., Lange, P., Narayan-Chen, A., Gella, S., Piramithu, R., Tur, G., and Hakkani-Tur, D · 2021
Later among the works it cites.
Interpretation of emergent communication in heterogeneous collaborative embodied agents
Patel, S., Wani, S., Jain, U., Schwing, A., Lazebnik, S., Savva, M., and Chang, A · 2021
Later among the works it cites.
Collaborating with humans without human data
Strouse, D., McKee, K., Botvinick, M., Hughes, E., and Everett, R · 2021
Later among the works it cites.
Habitat 2.0: Training home assistants to rearrange their habitat
Szot, A., Clegg, A., Undersander, E., Wijmans, E., Zhao, Y., Turner, J., Maestre, N., Mukadam, M., Chaplot, D., Maksymets, O., et al · 2021
Later among the works it cites.
Open-ended learning leads to generally capable agents
Team, O. E. L., Stooke, A., Mahajan, A., Barros, C., Deck, C., Bauer, J., Sygnowski, J., Trebacz, M., Jaderberg, M., Mathieu, M., et al · 2021
Later among the works it cites.
Too many cooks: Bayesian inference for coordinating multi-agent collaboration
Wu, S. A., Wang, R. E., Evans, J. A., Tenenbaum, J. B., Parkes, D. C., and Kleiman-Weiner, M · 2021
Later among the works it cites.
Maximum entropy population based training for zero-shot human-ai coordination
Zhao, R., Song, J., Haifeng, H., Gao, Y., Wu, Y., Sun, Z., and Wei, Y · 2021
Later among the works it cites.
Multi-skill mobile manipulation for object rearrangement
Gu, J., Chaplot, D. S., Su, H., and Malik, J · 2022
Later among the works it cites.
Quantifying the effects of environment and population diversity in multi-agent reinforcement learning
McKee, K. R., Leibo, J. Z., Beattie, C., and Everett, R · 2022
Later among the works it cites.
Towards robust ad hoc teamwork agents by creating diverse training teammates
Rahman, A., Fosong, E., Carlucho, I., and Albrecht, S. V · 2022
Later among the works it cites.
Symmetric machine theory of mind
Sclar, M., Neubig, G., and Bisk, Y · 2022
Later among the works it cites.
Habitat rearrangement challenge 2022
Szot, A., Yadav, K., Clegg, A., Berges, V.-P., Gokaslan, A., Chang, A., Savva, M., Kira, Z., and Batra, D · 2022
Later among the works it cites.
Co-gail: Learning diverse strategies for human-robot collaboration
Wang, C., Pérez-D’Arpino, C., Xu, D., Fei-Fei, L., Liu, K., and Savarese, S · 2022
Later among the works it cites.