mlsanityData to 14 Oct 2026

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

arXiv 1710.05627 ↗First posted 16 Oct 2017cs.AIcited by 20

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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.

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

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Citing papers per year: 2021: 10; 2022: 4; 2023: 3; 2024: 3.

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  1. 2021Learning Interpretable End-to-End Vision-Based Motion Planning for Autonomous Driving with Optical Flow Distillationcited by 25
  2. 2021Subgoal Search For Complex Reasoning Taskscited by 21
  3. 2021End-to-End Interactive Prediction and Planning with Optical Flow Distillation for Autonomous Drivingcited by 17
  4. 2023Real-time Neural Network Inference on Extremely Weak Devices: Agile Offloading with Explainable AIcited by 17
  5. 2022Fast and Precise: Adjusting Planning Horizon with Adaptive Subgoal Searchcited by 12
  6. 2021REGRAD: A Large-Scale Relational Grasp Dataset for Safe and Object-Specific Robotic Grasping in Cluttercited by 11
  7. 2021Carl-Lead: Lidar-based End-to-End Autonomous Driving with Contrastive Deep Reinforcement Learningcited by 11
  8. 2021Visual Perspective Taking for Opponent Behavior Modelingcited by 6
  9. 2021Deep Visual Navigation under Partial Observabilitycited by 6
  10. 2023Hybrid Search for Efficient Planning with Completeness Guaranteescited by 6
  11. 2021Hierarchical Robot Navigation in Novel Environments using Rough 2-D Mapscited by 4
  12. 2021Hierarchically Integrated Models: Learning to Navigate from Heterogeneous Robotscited by 3
8 more
  1. 2022MLNav: Learning to Safely Navigate on Martian Terrainscited by 3
  2. 2022Hybrid Deep Reinforcement Learning and Planning for Safe and Comfortable Automated Drivingcited by 3
  3. 2024Self-Supervised Interpretable End-to-End Learning via Latent Functional Modularitycited by 3
  4. 2024Combining RL and IL using a dynamic, performance-based modulation over learning signals and its application to local planningcited by 3
  5. 2022Generalizing to New Tasks via One-Shot Compositional Subgoalscited by 1
  6. 2023Virtual Guidance as a Mid-level Representation for Navigation with Augmented Realitycited by 1
  7. 2024Motion planning for off-road autonomous driving based on human-like cognition and weight adaptationcited by 1
  8. 2021A review of motion planning algorithms for intelligent roboticscited by 0