# Regression Planning Networks

arXiv: https://arxiv.org/abs/1909.13072 · HTML: https://mlsanity.com/paper/1909.13072/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2019.

## Built on by (most cited first, 30 of 32)

- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [Inner Monologue: Embodied Reasoning through Planning with Language Models](https://mlsanity.com/paper/2207.05608/) (2022)
- [ProgPrompt: Generating Situated Robot Task Plans using Large Language Models](https://mlsanity.com/paper/2209.11302/) (2022)
- [Gen2Sim: Scaling up Robot Learning in Simulation with Generative Models](https://mlsanity.com/paper/2310.18308/) (2023)
- [TASKOGRAPHY: Evaluating robot task planning over large 3D scene graphs](https://mlsanity.com/paper/2207.05006/) (2022)
- [LoTa-Bench: Benchmarking Language-oriented Task Planners for Embodied Agents](https://mlsanity.com/paper/2402.08178/) (2024)
- [Sequential Dexterity: Chaining Dexterous Policies for Long-Horizon Manipulation](https://mlsanity.com/paper/2309.00987/) (2023)
- [Tree-Planner: Efficient Close-loop Task Planning with Large Language Models](https://mlsanity.com/paper/2310.08582/) (2023)
- [Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous Driving and Zero-Shot Instruction Following](https://mlsanity.com/paper/2402.06559/) (2024)
- [Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal Reasoning](https://mlsanity.com/paper/2207.09081/) (2022)
- [LEAGUE: Guided Skill Learning and Abstraction for Long-Horizon Manipulation](https://mlsanity.com/paper/2210.12631/) (2022)
- [BUMBLE: Unifying Reasoning and Acting with Vision-Language Models for Building-wide Mobile Manipulation](https://mlsanity.com/paper/2410.06237/) (2024)
- [Energy-based Models are Zero-Shot Planners for Compositional Scene Rearrangement](https://mlsanity.com/paper/2304.14391/) (2023)
- [Predicting Stable Configurations for Semantic Placement of Novel Objects](https://mlsanity.com/paper/2108.12062/) (2021)
- [Generating Executable Action Plans with Environmentally-Aware Language Models](https://mlsanity.com/paper/2210.04964/) (2022)
- [A Survey of Optimization-based Task and Motion Planning: From Classical To Learning Approaches](https://mlsanity.com/paper/2404.02817/) (2024)
- [ReAcTree: Hierarchical LLM Agent Trees with Control Flow for Long-Horizon Task Planning](https://mlsanity.com/paper/2511.02424/) (2025)
- [Lifting Traces to Logic: Programmatic Skill Induction with Neuro-Symbolic Learning for Long-Horizon Agentic Tasks](https://mlsanity.com/paper/2605.01293/) (2026)
- [Bayesian Disturbance Injection: Robust Imitation Learning of Flexible Policies for Robot Manipulation](https://mlsanity.com/paper/2211.03393/) (2022)
- [PDSketch: Integrated Planning Domain Programming and Learning](https://mlsanity.com/paper/2303.05501/) (2023)
- [Structured Preference Optimization for Vision-Language Long-Horizon Task Planning](https://mlsanity.com/paper/2502.20742/) (2025)
- [Closed Loop Interactive Embodied Reasoning for Robot Manipulation](https://mlsanity.com/paper/2404.15194/) (2024)
- [Active Task Randomization: Learning Robust Skills via Unsupervised Generation of Diverse and Feasible Tasks](https://mlsanity.com/paper/2211.06134/) (2022)
- [RoboMP2: A Robotic Multimodal Perception-Planning Framework with Multimodal Large Language Models](https://mlsanity.com/paper/2404.04929/) (2024)
- [Learning Planning Abstractions from Language](https://mlsanity.com/paper/2405.03864/) (2024)
- [Learning to Recover from Plan Execution Errors during Robot Manipulation: A Neuro-symbolic Approach](https://mlsanity.com/paper/2405.18948/) (2024)
- [6-DoF Stability Field via Diffusion Models](https://mlsanity.com/paper/2310.17649/) (2023)
- [Stable Object Placement Planning From Contact Point Robustness](https://mlsanity.com/paper/2410.12483/) (2024)
- [VSGM -- Enhance robot task understanding ability through visual semantic graph](https://mlsanity.com/paper/2105.08959/) (2021)
- [Procedures as Programs: Hierarchical Control of Situated Agents through Natural Language](https://mlsanity.com/paper/2109.08214/) (2021)

## Abstract (arXiv)

Recent learning-to-plan methods have shown promising results on planning directly from observation space. Yet, their ability to plan for long-horizon tasks is limited by the accuracy of the prediction model. On the other hand, classical symbolic planners show remarkable capabilities in solving long-horizon tasks, but they require predefined symbolic rules and symbolic states, restricting their real-world applicability. In this work, we combine the benefits of these two paradigms and propose a learning-to-plan method that can directly generate a long-term symbolic plan conditioned on high-dimensional observations. We borrow the idea of regression (backward) planning from classical planning literature and introduce Regression Planning Networks (RPN), a neural network architecture that plans backward starting at a task goal and generates a sequence of intermediate goals that reaches the current observation. We show that our model not only inherits many favorable traits from symbolic planning, e.g., the ability to solve previously unseen tasks but also can learn from visual inputs in an end-to-end manner. We evaluate the capabilities of RPN in a grid world environment and a simulated 3D kitchen environment featuring complex visual scenes and long task horizons, and show that it achieves near-optimal performance in completely new task instances.