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The Limits and Potentials of Deep Learning for Robotics

arXiv 1804.06557 ↗First posted 18 Apr 2018cs.ROcited by 104

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Abstract (arXiv)

The application of deep learning in robotics leads to very specific problems and research questions that are typically not addressed by the computer vision and machine learning communities. In this paper we discuss a number of robotics-specific learning, reasoning, and embodiment challenges for deep learning. We explain the need for better evaluation metrics, highlight the importance and unique challenges for deep robotic learning in simulation, and explore the spectrum between purely data-driven and model-driven approaches. We hope this paper provides a motivating overview of important research directions to overcome the current limitations, and help fulfill the promising potentials of deep learning in robotics.

Key facts. Cited by 104 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Apr 2018.

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Citing papers per year: 2021: 27; 2022: 28; 2023: 21; 2024: 14; 2025: 10; 2026: 4.

104 papers.

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  1. 2021A Survey of Uncertainty in Deep Neural Networkscited by 675
  2. 2021How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learnedcited by 201
  3. 2024Deep Reinforcement Learning for Robotics: A Survey of Real-World Successescited by 153
  4. 2021Advances in adversarial attacks and defenses in computer vision: A surveycited by 127
  5. 2023Learning a Diffusion Model Policy from Rewards via Q-Score Matchingcited by 126
  6. 2022Robust Reinforcement Learning using Offline Datacited by 95
  7. 2025WorldGym: World Model as An Environment for Policy Evaluationcited by 61
  8. 2022Density-aware NeRF Ensembles: Quantifying Predictive Uncertainty in Neural Radiance Fieldscited by 58
  9. 2021Semantics for Robotic Mapping, Perception and Interaction: A Surveycited by 56
  10. 2023Improved Sample Complexity Bounds for Distributionally Robust Reinforcement Learningcited by 44
  11. 2021Adversarial Robustness with Semi-Infinite Constrained Learningcited by 39
  12. 2023Natural Actor-Critic for Robust Reinforcement Learning with Function Approximationcited by 35
38 more
  1. 2022SAFE: Sensitivity-Aware Features for Out-of-Distribution Object Detectioncited by 33
  2. 2021Uncertainty for Identifying Open-Set Errors in Visual Object Detectioncited by 26
  3. 2022Sample Efficient Deep Reinforcement Learning via Uncertainty Estimationcited by 26
  4. 2021Bayesian Deep Basis Fitting for Depth Completion with Uncertaintycited by 23
  5. 2022Interpretable Self-Aware Neural Networks for Robust Trajectory Predictioncited by 22
  6. 2023Deep Learning for Inertial Positioning: A Surveycited by 20
  7. 2021Distributionally Robust Policy Learning via Adversarial Environment Generationcited by 17
  8. 2024Deep Reinforcement Learning-based Obstacle Avoidance for Robot Movement in Warehouse Environmentscited by 17
  9. 2021Advances in Inference and Representation for Simultaneous Localization and Mappingcited by 16
  10. 2021Maximum Likelihood Constraint Inference from Stochastic Demonstrationscited by 15
  11. 2024Evaluating Uncertainty-based Failure Detection for Closed-Loop LLM Plannerscited by 15
  12. 2024Sampling from Energy-based Policies using Diffusioncited by 15
  13. 2025A Step Toward World Models: A Survey on Robotic Manipulationcited by 15
  14. 2023Deep Learning for Visual Localization and Mapping: A Surveycited by 14
  15. 2021Hyperdimensional Feature Fusion for Out-Of-Distribution Detectioncited by 13
  16. 2024Open-Set Recognition in the Age of Vision-Language Modelscited by 13
  17. 2021Multimodal Fusion of EMG and Vision for Human Grasp Intent Inference in Prosthetic Hand Controlcited by 12
  18. 2024Propose, Assess, Search: Harnessing LLMs for Goal-Oriented Planning in Instructional Videoscited by 12
  19. 2021Transferring Knowledge from Text to Video: Zero-Shot Anticipation for Procedural Actionscited by 10
  20. 2023LMC: Large Model Collaboration with Cross-assessment for Training-Free Open-Set Object Recognitioncited by 10
  21. 2024A Comprehensive Review of Knowledge Distillation in Computer Visioncited by 10
  22. 2021Fool Me Once: Robust Selective Segmentation via Out-of-Distribution Detection with Contrastive Learningcited by 9
  23. 2022Survey of Deep Learning for Autonomous Surface Vehicles in the Marine Environmentcited by 9
  24. 2022Sim-to-Real via Sim-to-Seg: End-to-end Off-road Autonomous Driving Without Real Datacited by 9
  25. 2021Hierarchical Reinforcement Learning for Air Combat at DARPA's AlphaDogfight Trialscited by 8
  26. 2022Dataset of Industrial Metal Objectscited by 8
  27. 2023MoCA: Memory-Centric, Adaptive Execution for Multi-Tenant Deep Neural Networkscited by 8
  28. 2021Active learning with MaskAL reduces annotation effort for training Mask R-CNNcited by 7
  29. 2022What's in the Black Box? The False Negative Mechanisms Inside Object Detectorscited by 7
  30. 2022SC-Explorer: Incremental 3D Scene Completion for Safe and Efficient Exploration Mapping and Planningcited by 6
  31. 2022A Survey of Computer Vision Technologies In Urban and Controlled-environment Agriculturecited by 6
  32. 2022A Survey on the Integration of Machine Learning with Sampling-based Motion Planningcited by 6
  33. 2025Finite-Sample Analysis of Policy Evaluation for Robust Average Reward Reinforcement Learningcited by 6
  34. 2026Embodied Foundation Models at the Edge: A Survey of Deployment Constraints and Mitigation Strategiescited by 6
  35. 2021Uncertainty-sensitive Activity Recognition: a Reliability Benchmark and the CARING Modelscited by 5
  36. 2022Physics-informed ConvNet: Learning Physical Field from a Shallow Neural Networkcited by 5
  37. 2022Utilizing Explainable AI for improving the Performance of Neural Networkscited by 5
  38. 2023Conformal Policy Learning for Sensorimotor Control Under Distribution Shiftscited by 5

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