# Intention-Net: Integrating Planning and Deep Learning for Goal-Directed Autonomous Navigation

arXiv: https://arxiv.org/abs/1710.05627 · HTML: https://mlsanity.com/paper/1710.05627/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2017.

## Built on by (most cited first, 20 of 20)

- [Learning Interpretable End-to-End Vision-Based Motion Planning for Autonomous Driving with Optical Flow Distillation](https://mlsanity.com/paper/2104.12861/) (2021)
- [Subgoal Search For Complex Reasoning Tasks](https://mlsanity.com/paper/2108.11204/) (2021)
- [End-to-End Interactive Prediction and Planning with Optical Flow Distillation for Autonomous Driving](https://mlsanity.com/paper/2104.08862/) (2021)
- [Real-time Neural Network Inference on Extremely Weak Devices: Agile Offloading with Explainable AI](https://mlsanity.com/paper/2312.14229/) (2023)
- [Fast and Precise: Adjusting Planning Horizon with Adaptive Subgoal Search](https://mlsanity.com/paper/2206.00702/) (2022)
- [REGRAD: A Large-Scale Relational Grasp Dataset for Safe and Object-Specific Robotic Grasping in Clutter](https://mlsanity.com/paper/2104.14118/) (2021)
- [Carl-Lead: Lidar-based End-to-End Autonomous Driving with Contrastive Deep Reinforcement Learning](https://mlsanity.com/paper/2109.08473/) (2021)
- [Visual Perspective Taking for Opponent Behavior Modeling](https://mlsanity.com/paper/2105.05145/) (2021)
- [Deep Visual Navigation under Partial Observability](https://mlsanity.com/paper/2109.07752/) (2021)
- [Hybrid Search for Efficient Planning with Completeness Guarantees](https://mlsanity.com/paper/2310.12819/) (2023)
- [Hierarchical Robot Navigation in Novel Environments using Rough 2-D Maps](https://mlsanity.com/paper/2106.03665/) (2021)
- [Hierarchically Integrated Models: Learning to Navigate from Heterogeneous Robots](https://mlsanity.com/paper/2106.13280/) (2021)
- [MLNav: Learning to Safely Navigate on Martian Terrains](https://mlsanity.com/paper/2203.04563/) (2022)
- [Hybrid Deep Reinforcement Learning and Planning for Safe and Comfortable Automated Driving](https://mlsanity.com/paper/2301.00650/) (2022)
- [Self-Supervised Interpretable End-to-End Learning via Latent Functional Modularity](https://mlsanity.com/paper/2403.18947/) (2024)
- [Combining RL and IL using a dynamic, performance-based modulation over learning signals and its application to local planning](https://mlsanity.com/paper/2405.09760/) (2024)
- [Generalizing to New Tasks via One-Shot Compositional Subgoals](https://mlsanity.com/paper/2205.07716/) (2022)
- [Virtual Guidance as a Mid-level Representation for Navigation with Augmented Reality](https://mlsanity.com/paper/2303.02731/) (2023)
- [Motion planning for off-road autonomous driving based on human-like cognition and weight adaptation](https://mlsanity.com/paper/2404.17820/) (2024)
- [A review of motion planning algorithms for intelligent robotics](https://mlsanity.com/paper/2102.02376/) (2021)

## Abstract (arXiv)

How can a delivery robot navigate reliably to a destination in a new office building, with minimal prior information? To tackle this challenge, this paper introduces a two-level hierarchical approach, which integrates model-free deep learning and model-based path planning. At the low level, a neural-network motion controller, called the intention-net, is trained end-to-end to provide robust local navigation. The intention-net maps images from a single monocular camera and "intentions" directly to robot controls. At the high level, a path planner uses a crude map, e.g., a 2-D floor plan, to compute a path from the robot's current location to the goal. The planned path provides intentions to the intention-net. Preliminary experiments suggest that the learned motion controller is robust against perceptual uncertainty and by integrating with a path planner, it generalizes effectively to new environments and goals.