Paper Citation Record · LEDGER

Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning

As of 7 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2506.08344.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.08344 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:19:09.106372Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

38 of 38 outbound references displayed

verified exact0
verified fuzzy25
unresolved13
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1c45b1cf-c44d-432b-81b3-346e07ec4349 · outbound

This paper cites A survey of robotic motion planning in dynamic environments,.

Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning A survey of robotic motion planning in dynamic environments,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:15.440366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bd2ce11c-b2b0-4ced-bab3-1fe567b4cd87 · outbound

This paper cites A survey of wheeled mobile manipulation: A decision-making perspective,.

Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning A survey of wheeled mobile manipulation: A decision-making perspective,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:14.276179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f9cc6d6c-b002-41aa-9c41-c4d2d4a1817f · outbound

This paper cites Plan-time multi-model switching for motion planning,.

Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning Plan-time multi-model switching for motion planning,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:14.002263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1b5d940a-c392-4e92-822f-48b5565e4142 · outbound

This paper cites Adaptive complexity model predictive control,.

Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning Adaptive complexity model predictive control,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T05:19:03.974793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f44beb5-afc9-4ae7-80e6-ce1f54639853 · outbound

This paper cites Real-time mixed-integer quadratic programming for vehicle decision-making and motion plan- ning,.

Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning Real-time mixed-integer quadratic programming for vehicle decision-making and motion plan- ning,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:13.808977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f05e84ae-6573-4b33-b10e-a5707eccd172 · outbound

This paper cites Multi-modal model predictive control through batch non-holonomic trajectory optimization: Application to highway driving,.

Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning Multi-modal model predictive control through batch non-holonomic trajectory optimization: Application to highway driving,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:13.631155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f0f08ce9-7514-4856-a033-49f1aea9b0f7 · outbound

This paper cites Manipulation plan- ning among movable obstacles using physics-based adaptive motion primitives,.

Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning Manipulation plan- ning among movable obstacles using physics-based adaptive motion primitives,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:13.474071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 06471a85-676c-4e78-8398-2493be3ce945 · outbound

This paper cites Constrained stochastic optimal control with learned importance sampling: A path integral approach,.

Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning Constrained stochastic optimal control with learned importance sampling: A path integral approach,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:13.278158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 64534089-77b3-4450-9e3d-55baba696291 · outbound

This paper cites A holistic approach to reactive mobile manipulation,.

Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning A holistic approach to reactive mobile manipulation,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T05:19:04.755511Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d14742d6-cc57-4cb1-8e3b-d362a1019cbf · outbound

This paper cites An architecture for reactive mobile manipulation on-the-move,.

Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning An architecture for reactive mobile manipulation on-the-move,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T05:19:04.941008Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df1fb7e0-981a-4510-9d76-59ff6d852bd6 · outbound

This paper cites Coupled mobile manip- ulation via trajectory optimization with free space decomposition,.

Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning Coupled mobile manip- ulation via trajectory optimization with free space decomposition,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:13.078291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 09590fd9-db49-4d79-ad96-51b7666cda9e · outbound

This paper cites A review of mobile robot motion planning methods: from classical motion planning workflows to reinforcement learning-based architectures,.

Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning A review of mobile robot motion planning methods: from classical motion planning workflows to reinforcement learning-based architectures,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:12.902360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1db14278-1cb9-41fa-ba3b-973080efef59 · outbound

This paper cites Perceptive model predictive control for continuous mobile manipulation,.

Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning Perceptive model predictive control for continuous mobile manipulation,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T05:19:05.370850Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d77c69ae-3492-42f2-b8e1-3236ef71dc71 · outbound

This paper cites A unified mpc framework for whole-body dynamic locomotion and manipula- tion,.

Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning A unified mpc framework for whole-body dynamic locomotion and manipula- tion,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T05:19:05.510631Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7390d85a-4a7e-4b45-aeac-b613a9fc5433 · outbound

This paper cites An efficient sequential linear quadratic algorithm for solving nonlinear optimal control problems,.

Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning An efficient sequential linear quadratic algorithm for solving nonlinear optimal control problems,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:12.705669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 11e51b77-a36e-4679-8e14-b68ca2e355fa · outbound

This paper cites Constraint handling in continuous-time ddp-based model predictive control,.

Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning Constraint handling in continuous-time ddp-based model predictive control,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:12.502315Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 347200e2-734d-4a68-8adb-7b773f8c87f4 · outbound

This paper cites Learning mobile manipulation through deep reinforcement learning,.

Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning Learning mobile manipulation through deep reinforcement learning,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:12.303009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation dccfa014-aba7-4e49-ba5f-086b04a80c1d · outbound

This paper cites Learning positioning policies for mobile manipulation operations with deep reinforcement learning,.

Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning Learning positioning policies for mobile manipulation operations with deep reinforcement learning,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:12.153491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0840f2ec-df31-4c74-98f5-81f103f82388 · outbound

This paper cites Hrl4in: Hierar- chical reinforcement learning for interactive navigation with mobile manipulators,.

Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning Hrl4in: Hierar- chical reinforcement learning for interactive navigation with mobile manipulators,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:11.993745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1f74c2a0-f104-49a9-be9b-fe55fbcd7413 · outbound

This paper cites An online training method for augmenting mpc with deep reinforcement learning,.

Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning An online training method for augmenting mpc with deep reinforcement learning,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:11.798223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2d21e420-5638-4673-86ca-de22eca1293a · outbound

This paper cites Motion planner augmented reinforcement learning for robot manipulation in obstructed environments,.

Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning Motion planner augmented reinforcement learning for robot manipulation in obstructed environments,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:11.624067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 48ece232-e76f-4379-868b-6d518abeb78b · outbound

This paper cites Relmogen: Integrating motion generation in reinforcement learning for mobile manipulation,.

Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning Relmogen: Integrating motion generation in reinforcement learning for mobile manipulation,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:11.442204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 683bdb27-8d69-4936-8888-3eb3e5bfaf30 · outbound

This paper cites Cacto: Continuous actor-critic with trajectory opti- mization - towards global optimality,.

Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning Cacto: Continuous actor-critic with trajectory opti- mization - towards global optimality,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:11.215738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5460d0ad-4ffc-4ace-8316-56b68a8e93d4 · outbound

This paper cites Data efficient reinforcement learning for legged robots,.

Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning Data efficient reinforcement learning for legged robots,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:11.012755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0522f0c5-23b4-4dc1-bb2f-619683ec1e7b · outbound

This paper cites Hierarchical evasive path planning using reinforcement learning and model predictive control,.

Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning Hierarchical evasive path planning using reinforcement learning and model predictive control,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:10.820445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b7740439-643c-4b0d-a6dd-3ff38ebabe60 · outbound

This paper cites OCS2: An open source library for optimal control of switched systems,.

Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning OCS2: An open source library for optimal control of switched systems,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T05:19:07.397084Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b20735e7-e531-4a3f-9e13-92abb8815a64 · outbound

This paper cites Stable-baselines3: Reliable reinforcement learning implementations,.

Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning Stable-baselines3: Reliable reinforcement learning implementations,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T05:19:07.536590Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 11cc06b3-0990-4ce7-88bc-e3926cf1e6f0 · outbound

This paper cites Pybullet, a python module for physics sim- ulation for games, robotics and machine learning,.

Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning Pybullet, a python module for physics sim- ulation for games, robotics and machine learning,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T05:19:07.685827Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 772ae1ce-5dd0-4c29-957f-8115995f971d · outbound

This paper cites A friction-based kinematic model for skid- steer wheeled mobile robots,.

Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning A friction-based kinematic model for skid- steer wheeled mobile robots,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:10.585341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c0c8f7ae-e6f8-4c9c-9e8e-dcb0f80342e8 · outbound

This paper cites Relaxed logarithmic barrier function based model predictive control of linear systems,.

Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning Relaxed logarithmic barrier function based model predictive control of linear systems,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:10.275158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4a590a0a-22a5-4285-999f-92cfb70adf8e · outbound

This paper cites Planning and acting in partially observable stochastic domains,.

Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning Planning and acting in partially observable stochastic domains,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T05:19:08.085530Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f84ff9a2-2e44-4725-832c-9a83633174b5 · outbound

This paper cites Deep rein- forcement learning based robot navigation in dynamic environments using occupancy values of motion primitives,.

Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning Deep rein- forcement learning based robot navigation in dynamic environments using occupancy values of motion primitives,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:09.935958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6a2762ef-dd90-4a10-9e54-6497f256102e · outbound

This paper cites Siciliano,Kinematics.

Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning Siciliano,Kinematics

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:09.716837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0f53e006-55bb-42b2-ba8b-3ca9b3ca3297 · outbound

This paper cites igibson 2.0: Object-centric simulation for robot learning of everyday household tasks,.

Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning igibson 2.0: Object-centric simulation for robot learning of everyday household tasks,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:19:09.413954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4f28dc1c-3f68-4734-9f40-35be2d48e45b · outbound

This paper cites Proximal Policy Optimization Algorithms.

Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning Proximal Policy Optimization Algorithms

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T05:19:08.777914Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 847e6f63-f4c3-476d-a05b-488c1e97c9db · outbound

This paper cites Soft actor-critic: Off- policy maximum entropy deep reinforcement learning with a stochastic actor,.

Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning Soft actor-critic: Off- policy maximum entropy deep reinforcement learning with a stochastic actor,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T05:19:08.961423Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59c1c4de-9654-4f05-ac92-f6591f5b1388 · outbound

This paper cites Human-level control through deep reinforcement learning,.

Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning Human-level control through deep reinforcement learning,

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T05:19:09.106372Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54f93462-557d-40cf-a2a6-f70b29df4308 · outbound

This paper cites Available: https://doi.org/10.1007/978-1-84628-642-1 2.

Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning Available: https://doi.org/10.1007/978-1-84628-642-1 2

Reference 103

Resolution: unresolved; no resolver link, observed 2026-08-07T05:19:08.546606Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.