# Data-driven Planning via Imitation Learning

arXiv: https://arxiv.org/abs/1711.06391 · HTML: https://mlsanity.com/paper/1711.06391/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2017.

## Built on by (most cited first, 30 of 36)

- [Diffusion-based Generation, Optimization, and Planning in 3D Scenes](https://mlsanity.com/paper/2301.06015/) (2023)
- [Vision-based Navigation with Language-based Assistance via Imitation Learning with Indirect Intervention](https://mlsanity.com/paper/1812.04155/) (2018)
- [Imitation Learning: Progress, Taxonomies and Challenges](https://mlsanity.com/paper/2106.12177/) (2021)
- [Multi-Robot Motion Planning with Diffusion Models](https://mlsanity.com/paper/2410.03072/) (2024)
- [Sequence Model Imitation Learning with Unobserved Contexts](https://mlsanity.com/paper/2208.02225/) (2022)
- [Better than Your Teacher: LLM Agents that learn from Privileged AI Feedback](https://mlsanity.com/paper/2410.05434/) (2024)
- [SelfD: Self-Learning Large-Scale Driving Policies From the Web](https://mlsanity.com/paper/2204.10320/) (2022)
- [Imperative Learning: A Self-supervised Neuro-Symbolic Learning Framework for Robot Autonomy](https://mlsanity.com/paper/2406.16087/) (2024)
- [PILOT: Efficient Planning by Imitation Learning and Optimisation for Safe Autonomous Driving](https://mlsanity.com/paper/2011.00509/) (2020)
- [Informed POMDP: Leveraging Additional Information in Model-Based RL](https://mlsanity.com/paper/2306.11488/) (2023)
- [Motion Planning Transformers: A Motion Planning Framework for Mobile Robots](https://mlsanity.com/paper/2106.02791/) (2021)
- [Learning Graph Search Heuristics](https://mlsanity.com/paper/2212.03978/) (2022)
- [Improved Policy Optimization for Online Imitation Learning](https://mlsanity.com/paper/2208.00088/) (2022)
- [A Theoretical Justification for Asymmetric Actor-Critic Algorithms](https://mlsanity.com/paper/2501.19116/) (2025)
- [Gained in Translation: Privileged Pairwise Judges Enhance Multilingual Reasoning](https://mlsanity.com/paper/2601.18722/) (2026)
- [IMWM: Intuition Models Complement World Models for Latent Planning](https://mlsanity.com/paper/2606.01626/) (2026)
- [Neural Weighted A*: Learning Graph Costs and Heuristics with Differentiable Anytime A*](https://mlsanity.com/paper/2105.01480/) (2021)
- [MLNav: Learning to Safely Navigate on Martian Terrains](https://mlsanity.com/paper/2203.04563/) (2022)
- [Imitating, Fast and Slow: Robust learning from demonstrations via decision-time planning](https://mlsanity.com/paper/2204.03597/) (2022)
- [Enhancing Inverse Reinforcement Learning through Encoding Dynamic Information in Reward Shaping](https://mlsanity.com/paper/2410.03847/) (2024)
- [To Distill or Decide? Understanding the Algorithmic Trade-off in Partially Observable Reinforcement Learning](https://mlsanity.com/paper/2510.03207/) (2025)
- [Sequential Decision Making with Expert Demonstrations under Unobserved Heterogeneity](https://mlsanity.com/paper/2404.07266/) (2024)
- [Model Predictive Adversarial Imitation Learning for Planning from Observation](https://mlsanity.com/paper/2507.21533/) (2025)
- [Learning Admissible Heuristics for A*: Theory and Practice](https://mlsanity.com/paper/2509.22626/) (2025)
- [Informed Asymmetric Actor-Critic: Leveraging Privileged Signals Beyond Full-State Access](https://mlsanity.com/paper/2509.26000/) (2025)
- [CLAMGen: Closed-Loop Arm Motion Generation via Multi-view Vision-Based RL](https://mlsanity.com/paper/2103.13267/) (2021)
- [Transformer-Enhanced Motion Planner: Attention-Guided Sampling for State-Specific Decision Making](https://mlsanity.com/paper/2404.19403/) (2024)
- [SLOPE: Search with Learned Optimal Pruning-based Expansion](https://mlsanity.com/paper/2406.04935/) (2024)
- [Causal Imitation Learning under Expert-Observable and Expert-Unobservable Confounding](https://mlsanity.com/paper/2502.07656/) (2025)
- [DAA*: Deep Angular A Star for Image-based Path Planning](https://mlsanity.com/paper/2507.09305/) (2025)

## Abstract (arXiv)

Robot planning is the process of selecting a sequence of actions that optimize for a task specific objective. The optimal solutions to such tasks are heavily influenced by the implicit structure in the environment, i.e. the configuration of objects in the world. State-of-the-art planning approaches, however, do not exploit this structure, thereby expending valuable effort searching the action space instead of focusing on potentially good actions. In this paper, we address the problem of enabling planners to adapt their search strategies by inferring such good actions in an efficient manner using only the information uncovered by the search up until that time. We formulate this as a problem of sequential decision making under uncertainty where at a given iteration a planning policy must map the state of the search to a planning action. Unfortunately, the training process for such partial information based policies is slow to converge and susceptible to poor local minima. Our key insight is that if we could fully observe the underlying world map, we would easily be able to disambiguate between good and bad actions. We hence present a novel data-driven imitation learning framework to efficiently train planning policies by imitating a clairvoyant oracle - an oracle that at train time has full knowledge about the world map and can compute optimal decisions. We leverage the fact that for planning problems, such oracles can be efficiently computed and derive performance guarantees for the learnt policy. We examine two important domains that rely on partial information based policies - informative path planning and search based motion planning. We validate the approach on a spectrum of environments for both problem domains, including experiments on a real UAV, and show that the learnt policy consistently outperforms state-of-the-art algorithms.