Data to 7 Oct 2026

Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization

arXiv 1603.00448 ↗First posted 1 Mar 2016cs.LG cs.AI cs.ROcited by 400

Not on the map. Why?

Cited by 247 in Offline RL and imitation learning from data, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Reinforcement learning can acquire complex behaviors from high-level specifications. However, defining a cost function that can be optimized effectively and encodes the correct task is challenging in practice. We explore how inverse optimal control (IOC) can be used to learn behaviors from demonstrations, with applications to torque control of high-dimensional robotic systems. Our method addresses two key challenges in inverse optimal control: first, the need for informative features and effective regularization to impose structure on the cost, and second, the difficulty of learning the cost function under unknown dynamics for high-dimensional continuous systems. To address the former challenge, we present an algorithm capable of learning arbitrary nonlinear cost functions, such as neural networks, without meticulous feature engineering. To address the latter challenge, we formulate an efficient sample-based approximation for MaxEnt IOC. We evaluate our method on a series of simulated tasks and real-world robotic manipulation problems, demonstrating substantial improvement over prior methods both in terms of task complexity and sample efficiency.

Key facts. Cited by 400 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Mar 2016.

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Citing papers per year: 2017: 1; 2018: 18; 2019: 31; 2020: 41; 2021: 74; 2022: 51; 2023: 65; 2024: 62; 2025: 32; 2026: 25.

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2017Deep reinforcement learning from human preferencescited by 3,846
2023VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Modelscited by 733
2023End-to-end Autonomous Driving: Challenges and Frontierscited by 485
2018Behavioral Cloning from Observationcited by 482
2018An Algorithmic Perspective on Imitation Learningcited by 455
2018Scalable agent alignment via reward modeling: a research directioncited by 438
2018A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progresscited by 294
2019Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observationscited by 256
2020Language Conditioned Imitation Learning over Unstructured Datacited by 208
2018Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learningcited by 203
2021How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learnedcited by 201
2024Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferencescited by 176

38 more

2019Goal-conditioned Imitation Learningcited by 159
2024Behavior Generation with Latent Actionscited by 159
2023Text2Reward: Reward Shaping with Language Models for Reinforcement Learningcited by 153
2023A Survey of Imitation Learning: Algorithms, Recent Developments, and Challengescited by 145
2023Getting aligned on representational alignmentcited by 142
2023Vision-Language Models as Success Detectorscited by 138
2023RoboCLIP: One Demonstration is Enough to Learn Robot Policiescited by 128
2022SE(3)-DiffusionFields: Learning smooth cost functions for joint grasp and motion optimization through diffusioncited by 125
2018Reinforcement and Imitation Learning for Diverse Visuomotor Skillscited by 124
2019Imitation Learning as f-Divergence Minimizationcited by 121
2020Primal Wasserstein Imitation Learningcited by 120
2021Learning Generalizable Robotic Reward Functions from "In-The-Wild" Human Videoscited by 116
2024GRAPE: Generalizing Robot Policy via Preference Alignmentcited by 106
2018Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flowcited by 103
2018Multi-Agent Generative Adversarial Imitation Learningcited by 99
2020Toward the Fundamental Limits of Imitation Learningcited by 96
2023Is RLHF More Difficult than Standard RL?cited by 96
2019Recent Advances in Imitation Learning from Observationcited by 92
2021Coarse-to-Fine Imitation Learning: Robot Manipulation from a Single Demonstrationcited by 92
2024Understanding Reinforcement Learning-Based Fine-Tuning of Diffusion Models: A Tutorial and Reviewcited by 92
2020Safe Imitation Learning via Fast Bayesian Reward Inference from Preferencescited by 73
2020Offline Learning from Demonstrations and Unlabeled Experiencecited by 72
2024RLHF from Heterogeneous Feedback via Personalization and Preference Aggregationcited by 71
2021Feedback in Imitation Learning: The Three Regimes of Covariate Shiftcited by 68
2020f-IRL: Inverse Reinforcement Learning via State Marginal Matchingcited by 67
2018Task-Embedded Control Networks for Few-Shot Imitation Learningcited by 65
2022How to Leverage Unlabeled Data in Offline Reinforcement Learningcited by 61
2019Multi-Agent Adversarial Inverse Reinforcement Learningcited by 60
2021Trajectory Prediction with Latent Belief Energy-Based Modelcited by 60
2019Wasserstein Adversarial Imitation Learningcited by 59
2021A Survey of Deep RL and IL for Autonomous Driving Policy Learningcited by 59
2019Deep Reinforcement Learning from Policy-Dependent Human Feedbackcited by 56
2021Imitation Learning: Progress, Taxonomies and Challengescited by 56
2023NeRF in the Palm of Your Hand: Corrective Augmentation for Robotics via Novel-View Synthesiscited by 56
2021Identifiability in inverse reinforcement learningcited by 55
2020Model-Based Inverse Reinforcement Learning from Visual Demonstrationscited by 52
2021Offline Meta-Reinforcement Learning with Online Self-Supervisioncited by 52
2026Robometer: Scaling General-Purpose Robotic Reward Models via Trajectory Comparisonscited by 51

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