mlsanityData to 7 Oct 2026

High-Speed Accurate Robot Control using Learned Forward Kinodynamics and Non-linear Least Squares Optimization

arXiv 2206.08487 ↗First posted 16 Jun 2022cs.RO cs.AI cs.LGcited by 9

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Abstract (arXiv)

Accurate control of robots at high speeds requires a control system that can take into account the kinodynamic interactions of the robot with the environment. Prior works on learning inverse kinodynamic (IKD) models of robots have shown success in capturing the complex kinodynamic effects. However, the types of control problems these approaches can be applied to are limited only to that of following pre-computed kinodynamically feasible trajectories. In this paper we present Optim-FKD, a new formulation for accurate, high-speed robot control that makes use of a learned forward kinodynamic (FKD) model and non-linear least squares optimization. Optim-FKD can be used for accurate, high speed control on any control task specifiable by a non-linear least squares objective. Optim-FKD can solve for control objectives such as path following and time-optimal control in real time, without needing access to pre-computed kinodynamically feasible trajectories. We empirically demonstrate these abilities of our approach through experiments on a scale one-tenth autonomous car. Our results show that Optim-FKD can follow desired trajectories more accurately and can find better solutions to optimal control problems than baseline approaches.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 25 of its 49 references that are in the map. First posted on arXiv 16 Jun 2022.

Builds on

25 of 49 references.

References · 25

  1. 2016End to End Learning for Self-Driving Carscited by 1,000
  2. 2022Socially Compliant Navigation Dataset (SCAND): A Large-Scale Dataset of Demonstrations for Social Navigationcited by 122
  3. 2016Decentralized Non-communicating Multiagent Collision Avoidance with Deep Reinforcement Learningcited by 104
  4. 2017Virtual-to-real Deep Reinforcement Learning: Continuous Control of Mobile Robots for Mapless Navigationcited by 90
  5. 2018Learning Navigation Behaviors End-to-End with AutoRLcited by 70
  6. 2022Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racingcited by 56
  7. 2014Batch Informed Trees (BIT*): Sampling-based Optimal Planning via the Heuristically Guided Search of Implicit Random Geometric Graphscited by 38
  8. 2016From Perception to Decision: A Data-driven Approach to End-to-end Motion Planning for Autonomous Ground Robotscited by 37
  9. 2018Reinforced Imitation: Sample Efficient Deep Reinforcement Learning for Map-less Navigation by Leveraging Prior Demonstrationscited by 35
  10. 2021VOILA: Visual-Observation-Only Imitation Learning for Autonomous Navigationcited by 27
  11. 2021Learning Inverse Kinodynamics for Accurate High-Speed Off-Road Navigation on Unstructured Terraincited by 23
  12. 2022VI-IKD: High-Speed Accurate Off-Road Navigation using Learned Visual-Inertial Inverse Kinodynamicscited by 19
13 more
  1. 2020APPLD: Adaptive Planner Parameter Learning from Demonstrationcited by 17
  2. 2021Visual Representation Learning for Preference-Aware Path Planningcited by 17
  3. 2020Stochastic Grounded Action Transformation for Robot Learning in Simulationcited by 16
  4. 2020APPLI: Adaptive Planner Parameter Learning From Interventionscited by 16
  5. 2021APPL: Adaptive Planner Parameter Learningcited by 15
  6. 2021APPLE: Adaptive Planner Parameter Learning from Evaluative Feedbackcited by 13
  7. 2020APPLR: Adaptive Planner Parameter Learning from Reinforcementcited by 11
  8. 2020Toward Agile Maneuvers in Highly Constrained Spaces: Learning from Hallucinationcited by 10
  9. 2021From Agile Ground to Aerial Navigation: Learning from Learned Hallucinationcited by 7
  10. 2020Agile Robot Navigation through Hallucinated Learning and Sober Deploymentcited by 6
  11. 2022Robot navigation from human demonstration: learning control behaviors with environment feature mapscited by 5
  12. 2022State Supervised Steering Function for Sampling-based Kinodynamic Planningcited by 3
  13. 2018Reduced Order Model Predictive Control For Setpoint Trackingcited by 1

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Cited by year

Citing papers per year: 2023: 4; 2024: 3; 2025: 2.

9 papers.

  1. 2023STERLING: Self-Supervised Terrain Representation Learning from Unconstrained Robot Experiencecited by 10
  2. 2024SELFI: Autonomous Self-Improvement with Reinforcement Learning for Social Navigationcited by 8
  3. 2023Wait, That Feels Familiar: Learning to Extrapolate Human Preferences for Preference Aligned Path Planningcited by 6
  4. 2023SOCIALGYM 2.0: Simulator for Multi-Agent Social Robot Navigation in Shared Human Spacescited by 5
  5. 2024VANP: Learning Where to See for Navigation with Self-Supervised Vision-Action Pre-Trainingcited by 5
  6. 2024Grounded Curriculum Learningcited by 5
  7. 2023A Study on Learning Social Robot Navigation with Multimodal Perceptioncited by 4
  8. 2025GACL: Grounded Adaptive Curriculum Learning with Active Task and Performance Monitoringcited by 3
  9. 2025T-CBF: Traversability-based Control Barrier Function to Navigate Vertically Challenging Terraincited by 1