mlsanityData to 7 Oct 2026

SIL-RRT*: Learning Sampling Distribution through Self Imitation Learning

arXiv 2411.17293 ↗First posted 26 Nov 2024cs.ROcited by 0

On the map: Offline RL and imitation learning from data → Robot manipulation imitation learning

Abstract (arXiv)

Efficiently finding safe and feasible trajectories for mobile objects is a critical field in robotics and computer science. In this paper, we propose SIL-RRT*, a novel learning-based motion planning algorithm that extends the RRT* algorithm by using a deep neural network to predict a distribution for sampling at each iteration. We evaluate SIL-RRT* on various 2D and 3D environments and establish that it can efficiently solve high-dimensional motion planning problems with fewer samples than traditional sampling-based algorithms. Moreover, SIL-RRT* is able to scale to more complex environments, making it a promising approach for solving challenging robotic motion planning problems.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Robot manipulation imitation learning, one of 13 directions in Offline RL and imitation learning from data. Builds on 24 of its 35 references that are in the map. First posted on arXiv 26 Nov 2024.

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24 of 35 references.

Closest ancestors · 1

Same direction, newest first

  1. 2021Self-Imitation Learning by Planning25 Mar

Other references · 23

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2016Generative Adversarial Imitation Learningcited by 1,576
  4. 2021Decision Transformer: Reinforcement Learning via Sequence Modelingcited by 1,528
  5. 2021Perceiver: General Perception with Iterative Attentioncited by 1,089
  6. 2021Perceiver IO: A General Architecture for Structured Inputs & Outputscited by 616
  7. 2019Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observationscited by 256
  8. 2018Self-Imitation Learningcited by 135
  9. 2019Imitation Learning from Imperfect Demonstrationcited by 112
  10. 2016Query-Efficient Imitation Learning for End-to-End Autonomous Drivingcited by 83
  11. 2017Learning Sampling Distributions for Robot Motion Planningcited by 79
  12. 2018Motion Planning Networkscited by 78
11 more
  1. 2014Informed RRT*: Optimal Sampling-based Path Planning Focused via Direct Sampling of an Admissible Ellipsoidal Heuristiccited by 50
  2. 2020Modeling Human Driving Behavior through Generative Adversarial Imitation Learningcited by 43
  3. 2014Batch Informed Trees (BIT*): Sampling-based Optimal Planning via the Heuristically Guided Search of Implicit Random Geometric Graphscited by 38
  4. 2019LEGO: Leveraging Experience in Roadmap Generation for Sampling-Based Planningcited by 19
  5. 2019Learning to Plan in High Dimensions via Neural Exploration-Exploitation Treescited by 18
  6. 2019RL-RRT: Kinodynamic Motion Planning via Learning Reachability Estimators from RL Policiescited by 17
  7. 2018Global overview of Imitation Learningcited by 16
  8. 2020Learning Obstacle Representations for Neural Motion Planningcited by 13
  9. 2020Graph Neural Networks for Motion Planningcited by 12
  10. 2022Learning-based Motion Planning in Dynamic Environments Using GNNs and Temporal Encodingcited by 5
  11. 2022NR-RRT: Neural Risk-Aware Near-Optimal Path Planning in Uncertain Nonconvex Environmentscited by 2

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  1. 2023Reinforcement Learning in Robotic Motion Planning by Combined Experience-based Planning and Self-Imitation Learning
  2. 2025Learning to Sample: Reinforcement Learning-Guided Sampling for Autonomous Vehicle Motion Planning
  3. 2020Flexible and Efficient Long-Range Planning Through Curious Exploration
  4. 2024Motion Planning Diffusion: Learning and Adapting Robot Motion Planning with Diffusion Models
  5. 2025Self-Augmented Robot Trajectory: Efficient Imitation Learning via Safe Self-augmentation with Demonstrator-annotated Precision
  6. 2025Conformalized Non-uniform Sampling Strategies for Accelerated Sampling-based Motion Planning ↗
  7. 2026Self-Imitated Diffusion Policy for Efficient and Robust Visual Navigation
  8. 2021A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles