Abstract
Increasing robotic perception and action capabilities promise to bring us closer to agents that are effective for automating complex operations in human-centered environments. However, to achieve the degree of flexibility and ease of use needed to apply such agents to new and diverse tasks, representations are required for generalizable reasoning about conditions and effects of interactions, and as common ground for communicating with non-expert human users. To advance the discussion on how to meet these challenges, we characterize open problems and point out promising research directions.
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References
Cadena, C., et al.: Past, present, and future of simultaneous localization and mapping: toward the robust-perception age. IEEE Trans. Robot. 32(6), 1309–1332 (2016)
Van den Berg, J., Lin, M., Manocha, D.: Reciprocal velocity obstacles for real-time multi-agent navigation. In: IEEE International Conference on Robotics and Automation (2008)
Gao, Y., Huang, C.M.: Evaluation of socially-aware robot navigation. Front. Robot. AI 8(721317), 420 (2021)
Alami, R., et al.: Safe and dependable physical human-robot interaction in anthropic domains: State of the art and challenges. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (2006)
Zacharaki, A., Kostavelis, I., Gasteratos, A., Dokas, I.: Safety bounds in human robot interaction: a survey. Saf. Sci. 127, 104667 (2020)
Florence, P., Manuelli, L., Tedrake, R.: Self-supervised correspondence in visuomotor policy learning. IEEE Robot. Autom. Lett. 5(2), 492–499 (2020)
Garg, S., et al.: Semantics for robotic mapping, perception and interaction: a survey. Found. Trends® Robot. 8(1–2), 1–224 (2020)
Narita, G., Seno, T., Ishikawa, T., Kaji, Y.: PanopticFusion: online volumetric semantic mapping at the level of stuff and things. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (2019)
Grinvald, M., et al.: Volumetric instance-aware semantic mapping and 3D object discovery. IEEE Robot. Autom. Lett. 4(3), 3037–3044 (2019)
Kothari, P., Kreiss, S., Alahi, A.: Human trajectory forecasting in crowds: a deep learning perspective. IEEE Trans. Intell. Transp. Syst. 23, 7386–7400 (2021)
Bohg, J., Morales, A., Asfour, T., Kragic, D.: Data-driven grasp synthesis - a survey. IEEE Trans. Robot. 30(2), 289–309 (2014)
Gualtieri, M., ten Pas, A., Saenko, K., Platt, R.: High precision grasp pose detection in dense clutter. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (2016)
Mahler, J., et al.: Learning ambidextrous robot grasping policies. Sci. Robot. 4(26), eaau4984 (2019)
Morrison, D., Corke, P., Leitner, J.: Learning robust, real-time, reactive robotic grasping. Int. J. Robot. Res. 39(2–3), 183–201 (2020)
Breyer, M., Chung, J.J., Ott, L., Siegwart, R., Nieto, J.: Volumetric grasping network: real-time 6 DOF grasp detection in clutter. In: Conference on Robot Learning (2021)
Mo, K., Guibas, L.J., Mukadam, M., Gupta, A., Tulsiani, S.: Where2Act: from pixels to actions for articulated 3D objects. In: IEEE/CVF International Conference on Computer Vision (2021)
Wu, R., et al.: VAT-Mart: learning visual action trajectory proposals for manipulating 3D articulated objects. In: International Conference on Learning Representations (2022)
Xu, Z., Zhanpeng, H., Song, S.: UMPNet: universal manipulation policy network for articulated objects. IEEE Robot. Autom. Lett. 7(2), 2447–2454 (2022)
Pierson, A., Vasile, C.I., Gandhi, A., Schwarting, W., Karaman, S., Rus, D.: Dynamic risk density for autonomous navigation in cluttered environments without object detection. In: International Conference on Robotics and Automation (2019)
Regier, P.: Robot navigation in cluttered environments. Ph.D. thesis, Rheinische Friedrich-Wilhelms-Universität Bonn (2022)
Karpas, E., Magazzeni, D.: Automated planning for robotics. Annu. Rev. Control Robot. Auton. Syst. 3, 417–439 (2019)
Fikes, R.E., Nilsson, N.J.: STRIPS: a new approach to the application of theorem proving to problem solving. Artif. Intell. 2(3–4), 189–208 (1971)
McDermott, D., et al.: PDDL: the planning domain definition language. Technical report, Yale Center for Computational Vision and Control (1998)
Garrett, C.R., Lozano-Pérez, T., Kaelbling, L.P.: FFRob: leveraging symbolic planning for efficient task and motion planning. Int. J. Robot. Res. 37(1), 104–136 (2018)
Konidaris, G., Kaelbling, L.P., Lozano-Perez, T.: From skills to symbols: learning symbolic representations for abstract high-level planning. J. Artif. Intell. Res. 61, 215–289 (2018)
Ames, B., Thackston, A., Konidaris, G.: Learning symbolic representations for planning with parameterized skills. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (2018)
Silver, T., Chitnis, R., Tenenbaum, J., Kaelbling, L.P., Lozano-Peréz, T.: Learning symbolic operators for task and motion planning. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (2021)
Yuan, W., Paxton, C., Desingh, K., Fox, D.: SORNet: spatial object-centric representations for sequential manipulation. In: Conference on Robot Learning (2022)
Shridhar, M., Manuelli, L., Fox, D.: CLIPort: what and where pathways for robotic manipulation. In: Conference on Robot Learning (2022)
Nair, A., Bahl, S., Khazatsky, A., Pong, V., Berseth, G., Levine, S.: Contextual imagined goals for self-supervised robotic learning. In: Conference on Robot Learning (2020)
Collins, J., Chand, S., Vanderkop, A., Howard, D.: A review of physics simulators for robotic applications. IEEE Access 9, 51416–51431 (2021)
Peng, X.B., Andrychowicz, M., Zaremba, W., Abbeel, P.: Sim-to-real transfer of robotic control with dynamics randomization. In: IEEE International Conference on Robotics and Automation, pp. 3803–3810 (2018)
Zhao, W., Queralta, J.P., Westerlund, T.: Sim-to-real transfer in deep reinforcement learning for robotics: a survey. In: IEEE Symposium Series on Computational Intelligence (2020)
Cohen, V., Burchfiel, B., Nguyen, T., Gopalan, N., Tellex, S., Konidaris, G.: Grounding language attributes to objects using Bayesian eigenobjects. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (2019)
Wald, J., Dhamo, H., Navab, N., Tombari, F.: Learning 3D semantic scene graphs from 3D indoor reconstructions. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (2020)
Gopalan, N., Rosen, E., Konidaris, G., Tellex, S.: Simultaneously learning transferable symbols and language groundings from perceptual data for instruction following. In: Proceedings of Robotics: Science and Systems, Corvalis, Oregon, USA, July (2020). https://doi.org/10.15607/RSS.2020.XVI.102
Rodríguez-Moreno, I., Martínez-Otzeta, J.M., Sierra, B., Rodriguez, I., Jauregi, E.: Video activity recognition: state-of-the-art. Sensors 19(14), 3160 (2019)
Torabi, F., Warnell, G., Stone, P.: Behavioral cloning from observation. In: International Joint Conference on Artificial Intelligence, pp. 4950–4957 (2018)
Bıyık, E., Losey, D.P., Palan, M., Landolfi, N.C., Shevchuk, G., Sadigh, D.: Learning reward functions from diverse sources of human feedback: optimally integrating demonstrations and preferences. Int. J. Robot. Res. 41(1), 45–67 (2022)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems (2012)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: IEEE Conference on Computer Vision and Pattern Recognition (2016)
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: IEEE International Conference on Computer Vision (2017)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems (2017)
Brown, T., Mann, B., Ryder, N., Subbiah, M., et al.: Language models are few-shot learners. In: Advances in Neural Information Processing Systems (2020)
Belta, C., Bicchi, A., Egerstedt, M., Frazzoli, E., Klavins, E., Pappas, G.J.: Symbolic planning and control of robot motion [Grand challenges of robotics]. IEEE Robot. Autom. Mag. 14(1), 61–70 (2007)
Kress-Gazit, H., Fainekos, G.E., Pappas, G.J.: Temporal-logic-based reactive mission and motion planning. IEEE Trans. Robot. 25(6), 1370–1381 (2009)
Mo, K., Qin, Y., Xiang, F., Su, H., Guibas, L.: O2O-afford: annotation-free large-scale object-object affordance learning. In: Conference on Robot Learning (2022)
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Chung, J.J., Förster, J., Wulkop, P., Ott, L., Lawrance, N., Siegwart, R. (2023). It’s Just Semantics: How to Get Robots to Understand the World the Way We Do. In: Billard, A., Asfour, T., Khatib, O. (eds) Robotics Research. ISRR 2022. Springer Proceedings in Advanced Robotics, vol 27. Springer, Cham. https://doi.org/10.1007/978-3-031-25555-7_1
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DOI: https://doi.org/10.1007/978-3-031-25555-7_1
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