Fetching the paper…
Reading the bibliography…
We propose a novel general method that finds action-grounded, discrete object and effect categories and builds probabilistic rules over them for non-trivial action planning.
ACNMP: Skill transfer and task extrapolation through learning from demonstration and reinforcement learning via representation sharing
Akbulut, M., Oztop, E., Seker, M. Y., Hh, X., Tekden, A., and Ugur, E. (2021) · 1907
Earlier work this paper cites.
Symbol formation
Werner, H., and Kaplan, B. (1963) · 1963
Earlier work this paper cites.
The symbol grounding problem
Harnad, S. (1990) · 1990
Earlier work this paper cites.
Learning concepts from sensor data of a mobile robot
Klingspor, V., Morik, K., and Rieger, A. D. (1996) · 1996
Earlier work this paper cites.
Introduction to AI Robotics
Murphy, R., and Murphy, R. R. (2000) · 2000
Earlier work this paper cites.
Symbol grounding: A new look at an old idea
Sun, R. (2000) · 2000
Earlier work this paper cites.
The BarrettHand grasper-programmably flexible part handling and assembly
Townsend, W. (2000) · 2000
Earlier work this paper cites.
The FF planning system: Fast plan generation through heuristic search
Hoffmann, J., and Nebel, B. (2001) · 2001
Earlier work this paper cites.
PPDDL1.0: An extension to PDDL for expressing planning domains with probabilistic effects
Younes, H. L., and Littman, M. L. (2004) · 2004
Earlier work this paper cites.
mGPT: A probabilistic planner based on heuristic search
Bonet, B., and Geffner, H. (2005) · 2005
Earlier work this paper cites.
Experiments in subsymbolic action planning with mobile robots
Pisokas, J., and Nehmzow, U. (2005) · 2005
Earlier work this paper cites.
Reducing the dimensionality of data with neural networks
Hinton, G. E., and Salakhutdinov, R. R. (2006) · 2006
Earlier work this paper cites.
Using kernel perceptrons to learn action effects for planning
Mourao, K., Petrick, R. P., and Steedman, M. (2008) · 2008
Earlier work this paper cites.
Representation and integration: Combining robot control, high-level planning, and action learning
Petrick, R., Kraft, D., Mourao, K., Pugeault, N., Krüger, N., and Steedman, M. (2008) · 2008
Earlier work this paper cites.
Cognitive agents: A procedural perspective relying on the predictability of Object-Action-Complexes OACs
Wörgötter, F., Agostini, A., Krüger, N., Shylo, N., and Porr, B. (2009) · 2009
Earlier work this paper cites.
Goal emulation and planning in perceptual space using learned affordances
Ugur, E., Oztop, E., and Sahin, E. (2011) · 2011
Earlier work this paper cites.
Efficient backprop
LeCun, Y. A., Bottou, L., Orr, G. B., and Müller, K.-R. (2012) · 2012
Earlier work this paper cites.
Self-discovery of motor primitives and learning grasp affordances
Ugur, E., Şahin, E., and Oztop, E. (2012) · 2012
Earlier work this paper cites.
Estimating or propagating gradients through stochastic neurons for conditional computation
Bengio, Y., Léonard, N., and Courville, A. C. (2013) · 2013
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D. P., and Welling, M. (2013) · 2013
Earlier work this paper cites.
Active learning for teaching a robot grounded relational symbols
Kulick, J., Toussaint, M., Lang, T., and Lopes, M. (2013) · 2013
Cited alongside, same era.
CoppeliaSim (formerly V-REP): A versatile and scalable robot simulation framework
Rohmer, E., Singh, S. P. N., and Freese, M. (2013) · 2013
Cited alongside, same era.
The ecological approach to visual perception: Classic edition
Gibson, J. J. (2014) · 2014
Cited alongside, same era.
Constructing symbolic representations for high-level planning
Konidaris, G., Kaelbling, L., and Lozano-Perez, T. (2014) · 2014
Cited alongside, same era.
Sequence to sequence learning with neural networks
Sutskever, I., Vinyals, O., and Le, Q. V. (2014) · 2014
Cited alongside, same era.
The Development of Symbolic Representation
Callaghan, T., and Corbit, J. (2015) · 2015
Cited alongside, same era.
From skills to symbols: Learning symbolic representations for abstract high-level planning
Konidaris, G., Kaelbling, L. P., and Lozano-Perez, T. (2018) · 2018
Later among the works it cites.
The complexity and generality of learning answer set programs
Law, M., Russo, A., and Broda, K. (2018) · 2018
Later among the works it cites.
On the convergence of Adam and beyond
Reddi, S. J., Kale, S., and Kumar, S. (2018) · 2018
Later among the works it cites.
Symbol emergence in cognitive developmental systems: a survey
Taniguchi, T., Ugur, E., Hoffmann, M., Jamone, L., Nagai, T., Rosman, B., Matsuka, T., Iwahashi, N., Oztop, E., Piater, J., et al. (2018) · 2018
Later among the works it cites.
On the necessity of abstraction
Konidaris, G. (2019) · 2019
Later among the works it cites.
Commonsense reasoning and knowledge acquisition to guide deep learning on robots.
Mota, T., and Sridharan, M. (2019) · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S., and Szegedy, C. (2015) · 2015
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D. P., and Ba, J. (2015) · 2015
Cited alongside, same era.
Symbol acquisition for probabilistic high-level planning
Konidaris, G., Kaelbling, L., and Lozano-Perez, T. (2015) · 2015
Cited alongside, same era.
Bottom-up learning of object categories, action effects and logical rules: From continuous manipulative exploration to symbolic planning
Ugur, E., and Piater, J. (2015a) · 2015
Cited alongside, same era.
Refining discovered symbols with multi-step interaction experience
Ugur, E., and Piater, J. (2015b) · 2015
Cited alongside, same era.
The Malmo platform for artificial intelligence experimentation.
Johnson, M., Hofmann, K., Hutton, T., and Bignell, D. (2016) · 2016
Cited alongside, same era.
Later among the works it cites.
Integrating non-monotonic logical reasoning and inductive learning with deep learning for explainable visual question answering
Riley, H., and Sridharan, M. (2019) · 2019
Later among the works it cites.
Conditional neural movement primitives
Seker, M. Y., Imre, M., Piater, J., and Ugur, E. (2019) · 2019
Later among the works it cites.
Mastering atari with discrete world models
Hafner, D., Lillicrap, T. P., Norouzi, M., and Ba, J. (2020) · 2020
Closest in time.
Learning portable representations for high-level planning
James, S., Rosman, B., and Konidaris, G. (2020) · 2020
Closest in time.
High-level representations through unconstrained sensorimotor learning
Ozturkcu, O. B., Ugur, E., and Oztop, E. (2020) · 2020
Closest in time.
Artificial Intelligence: A Modern Approach
Russell, S. J., and Norvig, P. (2020) · 2020
Closest in time.
The UR10 collaborative industrial robot
Universal Robots (2012) · 2020
Closest in time.
Learning neural-symbolic descriptive planning models via cube-space priors: The voyage home (to STRIPS)
Asai, M., and Muise, C. (2021) · 2021
Closest in time.
Learning neuro-symbolic relational transition models for bilevel planning
Chitnis, R., Silver, T., Tenenbaum, J. B., Perez, T., and Kaelbling, L. P. (2021) · 2021
Closest in time.
Learning symbolic operators for task and motion planning
Silver, T., Chitnis, R., Tenenbaum, J., Kaelbling, L. P., and Lozano-Pérez, T. (2021) · 2021
Closest in time.
Deep affordance foresight: Planning through what can be done in the future
Xu, D., Mandlekar, A., Martín-Martín, R., Zhu, Y., Savarese, S., and Fei-Fei, L. (2021) · 2021
Closest in time.
Classical planning in deep latent space
Asai, M., Kajino, H., Fukunaga, A., and Muise, C. (2022) · 2022
Closest in time.
Sornet: Spatial object-centric representations for sequential manipulation
Yuan, W., Paxton, C., Desingh, K., and Fox, D. (2022) · 2022
Closest in time.