Trajectory Planning for Non-Communicating Mobile Robots using Inverse Optimal Control

Nina Majer; S\"oren Hohmann; Stefan Schwab; Xin Ye; Yannick Epple

REVIEW 2 major objections 1 minor 3 references

Non-communicating robots reach goals 9.8% faster by estimating each other's targets with inverse optimal control and solving joint predictions.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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T0 review · grok-4.3

2026-06-28 22:30 UTC pith:GZX6BZO2

load-bearing objection The paper folds IOC goal estimation into a mutual perspective-taking loop for joint prediction and planning, reporting a 9.8% median time improvement in small simulations but without isolating whether the IOC estimates actually drive the gains. the 2 major comments →

arxiv 2605.30906 v1 pith:GZX6BZO2 submitted 2026-05-29 cs.RO cs.SYeess.SY

Trajectory Planning for Non-Communicating Mobile Robots using Inverse Optimal Control

Nina Majer , Yannick Epple , Xin Ye , Stefan Schwab , S\"oren Hohmann This is my paper

classification cs.RO cs.SYeess.SY

keywords trajectory planninginverse optimal controlmobile robotscollision avoidancegoal estimationmulti-robot coordinationprediction

verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper shows that mobile robots can plan collision-free trajectories more efficiently by using inverse optimal control to infer unknown goal states from observed past movements. Each robot then accounts for how others might predict its own motion and solves a combined prediction problem before planning its path. This leads to simulations where groups of 2 to 8 robots complete their tasks with a median 9.8 percent shorter time than when using simpler constant-acceleration goal estimates, and the planner always finds a solution. A reader would care because shared robot spaces require anticipating others without direct communication to avoid delays or deadlocks.

Core claim

By applying inverse optimal control to estimate goal states from past trajectories and having each robot consider the perspectives of others in a joint prediction step, the resulting predictions improve the quality of trajectory planning for non-communicating mobile robots in collision avoidance scenarios.

What carries the argument

Inverse optimal control for goal-state estimation from trajectories, combined with mutual perspective-taking in joint prediction.

Load-bearing premise

Inverse optimal control on observed trajectories produces goal estimates that are sufficiently accurate to improve planning over constant-acceleration baselines.

What would settle it

Running the same simulations with the proposed method and finding that goal-reaching times are not shorter or that solvers fail in some cases would disprove the improvement.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

The median time for all robots to reach goals decreases by 9.8 percent compared to constant-acceleration estimates.
The planning and prediction problems remain solvable in all tested cases with 2-8 robots.
The method applies to scenarios without communication between robots.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Physical robot experiments could test whether sensor inaccuracies reduce the accuracy of goal estimates from inverse optimal control.
This approach might apply to mixed human-robot environments where human goals are inferred similarly.
Scaling to larger numbers of robots would require checking if the joint prediction remains computationally feasible.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Desk Editor's Note

The paper folds IOC goal estimation into a mutual perspective-taking loop for joint prediction and planning, reporting a 9.8% median time improvement in small simulations but without isolating whether the IOC estimates actually drive the gains.

read the letter

The core move here is using inverse optimal control on observed trajectories to guess each robot's goal, then having every robot run a joint prediction from the others' viewpoints before planning its own path. In 2-8 robot simulations this yields a 9.8% faster median completion time versus a constant-acceleration baseline for goal estimates, and the solver never fails to find a solution.

The combination itself is a reasonable extension of existing IOC work to the non-communicating multi-agent case, and the mutual perspective step is a clean way to close the loop between prediction and planning. The consistent solver success is a practical detail worth noting.

The evidence is limited in a few straightforward ways. There is no separate quantification of how accurate the IOC goal estimates actually are, so it is unclear whether the reported speed-up comes from better estimates or from the joint-prediction machinery. The comparison is only to one baseline, with no ablations, no trial counts, no variance numbers, and no statistical tests. Everything stays in simulation, so transfer to hardware remains open.

This is for robotics researchers focused on decentralized collision avoidance and IOC applications. A reader already working in that niche could pick up the planning-prediction structure as an idea to try, but the lack of targeted validation on the IOC component keeps the result preliminary.

I would send it to peer review. The idea is coherent, the simulation outcomes are positive on their own terms, and the gaps are fixable with more targeted experiments rather than fundamental flaws.

Referee Report

2 major / 1 minor

Summary. The manuscript proposes a combined trajectory planning and prediction algorithm for non-communicating mobile robots. Inverse optimal control (IOC) is applied to observed past trajectories to estimate unknown goal states. Each robot performs mutual perspective-taking and solves a joint prediction problem using the estimated goals; the resulting predictions are incorporated into the planner. Simulations with 2–8 robots report a 9.8 % reduction in the median time until all robots reach their goals relative to a constant-acceleration baseline, together with zero solver failures on the planning or prediction problems.

Significance. If the reported performance gain is reproducible and attributable to the IOC component, the approach would offer a practical method for improving efficiency and reliability in multi-robot collision-avoidance settings without explicit communication. The claimed absence of solver failures is a concrete operational advantage. However, the lack of separate validation of goal-estimate accuracy and of ablation experiments limits the strength of the central claim that IOC-driven mutual prediction is the source of the observed improvement.

major comments (2)

[Abstract] Abstract / Simulation results paragraph: the 9.8 % median improvement and zero solver-failure claim are stated without any description of the number of Monte-Carlo trials, variance or inter-quartile range, statistical significance testing, or the precise robot dynamics and scenario generation procedure. These omissions prevent verification that the data support the stated quantitative claim.
[Simulation results] Simulation results: no quantitative evaluation of IOC goal-estimate fidelity (e.g., Euclidean or angular error relative to ground-truth goals) is provided, nor is an ablation presented that isolates the contribution of the IOC estimates from the joint-prediction machinery itself. Because the central claim attributes the 9.8 % gain to accurate IOC-based goal inference plus mutual perspective-taking, the absence of these checks is load-bearing.

minor comments (1)

[Abstract] The abstract would be clearer if it briefly indicated the robot kinematic model and the precise IOC objective function being inverted.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which highlight important aspects of experimental reporting and validation. We address each major comment below and indicate the revisions we will make to improve verifiability while remaining faithful to the work performed.

read point-by-point responses

Referee: [Abstract] Abstract / Simulation results paragraph: the 9.8 % median improvement and zero solver-failure claim are stated without any description of the number of Monte-Carlo trials, variance or inter-quartile range, statistical significance testing, or the precise robot dynamics and scenario generation procedure. These omissions prevent verification that the data support the stated quantitative claim.

Authors: We agree that these details are required for reproducibility. In the revised manuscript we will expand both the abstract and the simulation-results section to report the number of Monte-Carlo trials, inter-quartile ranges and other variance measures, any statistical tests performed, and a precise description of the robot dynamics and scenario-generation procedure. revision: yes
Referee: [Simulation results] Simulation results: no quantitative evaluation of IOC goal-estimate fidelity (e.g., Euclidean or angular error relative to ground-truth goals) is provided, nor is an ablation presented that isolates the contribution of the IOC estimates from the joint-prediction machinery itself. Because the central claim attributes the 9.8 % gain to accurate IOC-based goal inference plus mutual perspective-taking, the absence of these checks is load-bearing.

Authors: The referee is correct that direct goal-estimation error metrics and an ablation isolating IOC from joint prediction are absent. The existing comparison is against a constant-acceleration baseline that also uses estimated goals, so the observed 9.8 % difference is attributable to the IOC component under the joint-prediction framework; however, we did not compute ground-truth goal errors or run separate ablations. In revision we will add a clarifying paragraph on the role of each component and explicitly note the lack of these checks as a limitation. New ablation experiments are outside the scope of the current study. revision: partial

Circularity Check

0 steps flagged

No circularity; empirical comparison to independent baseline

full rationale

The paper applies IOC to estimate goal states from observed trajectories, then incorporates those estimates into joint prediction and planning. Performance is measured by an external metric (median time to goal in 2-8 robot simulations) against a separate constant-acceleration baseline. No derivation step reduces by construction to a fitted parameter, self-definition, or self-citation chain; the 9.8% improvement is reported as an empirical outcome, not a tautology. The method is self-contained against the stated baseline.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no concrete information on free parameters, background axioms, or newly postulated entities; all ledger entries therefore left empty.

pith-pipeline@v0.9.1-grok · 5664 in / 1085 out tokens · 24994 ms · 2026-06-28T22:30:08.217338+00:00 · methodology

0 comments

read the original abstract

To enable an efficient interaction of non-communicating mobile robots in collision avoidance scenarios, we present a novel combined trajectory planning and prediction algorithm. Inverse optimal control is used to estimate unknown goal states of all robots based on observed past trajectories. Each robot also takes the perspective of other robots in considering self-prediction and solves a joint prediction problem using the estimated goal states. The resulting predictions are then considered for planning. Simulation results of scenarios with 2-8 robots show that the median of the durations until all vehicles reach their goals is 9.8 % faster compared to planning with constant acceleration based estimated goal states. Moreover, the proposed approach never leads to the solver being unable to find a solution to the planning or prediction problem.

Figures

Figures reproduced from arXiv: 2605.30906 by Nina Majer, S\"oren Hohmann, Stefan Schwab, Xin Ye, Yannick Epple.

**Figure 1.** Figure 1: Comparison of the combined planning and predic [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗

discussion (0)

Reference graph

Works this paper leans on

3 extracted references · 2 canonical work pages

[1]

Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., and Diehl, M. (2019). Casadi: A software framework for non- linear optimization and optimal control.Mathematical Programming Computation, 11(1), 1–36. Gil, R.C., Ornia, D.J., Mustafa, K.A., and Alonso Mora, J. (2025). Predictability awareness for efficient and robust multi-agent coordination. InProc...

work page arXiv 2019
[2]

Karle, P., Geisslinger, M., Betz, J., and Lienkamp, M. (2022). Scenario understanding and motion predic- tion for autonomous vehicles—review and comparison. IEEE Transactions on Intelligent Transportation Sys- tems, 23(10), 16962–16982. Khan, H.I., Li, J., and Fridovich-Keil, D. (2025). What do agents think others would do? level-2 inverse games for infer...

work page arXiv 2022
[3]

Le Cleac’h, S., Schwager, M., and Manchester, Z. (2021). Lucidgames: Online unscented inverse dynamic games for adaptive trajectory prediction and planning.IEEE Robotics and Automation Letters, 6(3), 5485–5492. Liu, X., Peters, L., and Alonso-Mora, J. (2023). Learn- ing to play trajectory games against opponents with unknown objectives.IEEE Robotics and A...

2021

[1] [1]

Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., and Diehl, M. (2019). Casadi: A software framework for non- linear optimization and optimal control.Mathematical Programming Computation, 11(1), 1–36. Gil, R.C., Ornia, D.J., Mustafa, K.A., and Alonso Mora, J. (2025). Predictability awareness for efficient and robust multi-agent coordination. InProc...

work page arXiv 2019

[2] [2]

Karle, P., Geisslinger, M., Betz, J., and Lienkamp, M. (2022). Scenario understanding and motion predic- tion for autonomous vehicles—review and comparison. IEEE Transactions on Intelligent Transportation Sys- tems, 23(10), 16962–16982. Khan, H.I., Li, J., and Fridovich-Keil, D. (2025). What do agents think others would do? level-2 inverse games for infer...

work page arXiv 2022

[3] [3]

Le Cleac’h, S., Schwager, M., and Manchester, Z. (2021). Lucidgames: Online unscented inverse dynamic games for adaptive trajectory prediction and planning.IEEE Robotics and Automation Letters, 6(3), 5485–5492. Liu, X., Peters, L., and Alonso-Mora, J. (2023). Learn- ing to play trajectory games against opponents with unknown objectives.IEEE Robotics and A...

2021