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arxiv: 2605.26430 · v2 · pith:DI5EB67Knew · submitted 2026-05-26 · 💻 cs.RO

Multi-Robot Box Transport over Different Surfaces with Decentralized Role-based Proportional Control

Aditya Bhatt , Himavarshini Yarragangu , Urvish Shah , Venkata Sai Yaswanth Mohan Thota , Souma Chowdhury This is my paper

Pith reviewed 2026-06-29 17:42 UTC · model grok-4.3

classification 💻 cs.RO
keywords multi-robot transportdecentralized controlrole assignmentproportional controlbox pushingsimulation validationphysical experiment
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The pith

Robots transport boxes over varied surfaces by assigning decentralized roles and using proportional control based on local observations.

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

The paper presents R2P2, a method for multiple robots to collaboratively push rectangular boxes across flat, uphill, and downhill terrains with different friction. It assigns roles such as push, support, and prevent to each robot using rules that depend on whether the task requires rotation or translation of the box. Each robot then applies either rule-based or proportional velocity control. This decentralized approach is tested in simulation with six robots and physically with four TurtleBots moving a 1.2 kg box, showing better performance than a virtual leader-follower method across different masses and surface conditions.

Core claim

R2P2 enables asynchronous decentralized task and motion planning for box transport by assigning roles based on manipulation mode and applying proportional control primitives, achieving generalizability across surface friction, inclination, and box mass scenarios with higher success rates than standard methods.

What carries the argument

Role assignment mechanism that determines push, support or prevent actions from rules cognizant of box rotation versus translation mode, followed by proportional control of robot velocity.

If this is right

  • Reduces need for communication, synchronization and consensus among robots.
  • Mitigates single point of failure by avoiding central coordination.
  • Generalizes to different surface properties and box masses in both simulation and physical setups.
  • Outperforms virtual-leader-follower method in success rate for six-robot teams.

Where Pith is reading between the lines

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

  • Could extend to irregular objects or dynamic obstacles if role rules are adapted.
  • May scale to larger teams if observation assumptions hold.
  • Applicable to warehouse or disaster response scenarios where surfaces vary.

Load-bearing premise

Each robot can observe its own location and heading as well as those of the box.

What would settle it

A trial where robots lack box heading observations or face unmodeled surface changes leading to failure in role assignment.

Figures

Figures reproduced from arXiv: 2605.26430 by Aditya Bhatt, Himavarshini Yarragangu, Souma Chowdhury, Urvish Shah, Venkata Sai Yaswanth Mohan Thota.

Figure 1
Figure 1. Figure 1: Collaborative Box transport using a team of turtlebots [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Global and local coordinate system of the object [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Flowchart of R2P2 corners can quickly get out of the box due to uncertain con￾tact dynamics. Therefore, the robots are assigned locations at some δ distance from the edges. Considering these two criteria, the initial relative robot location assigned for a team of 6 robots is shown in [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Box primitive for three different target waypoints [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Virtual leader follower. The Figure on the left shows [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Demonstration of R2P2 over various terrains in IsaacSim. The up-hill and down-hill transport is over an inclined [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Analysis of the robot states and controls, categorised [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Main effects of heuristic control parameters on task [PITH_FULL_IMAGE:figures/full_fig_p009_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Main effects of environment factors on task comple [PITH_FULL_IMAGE:figures/full_fig_p010_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Physical and simulation experiment with R2P2. Plots (a) and (b) show the resultant box trajectory in the physical [PITH_FULL_IMAGE:figures/full_fig_p012_10.png] view at source ↗
read the original abstract

Collaborative transport of objects via pushing by multiple robots has many applications, ranging from construction and warehouse environments to post disaster debris clean-up. Achieving collaborative transport over surfaces with different inclination and friction properties however poses unique challenges. To address these challenges, this paper presents an asynchronous decentralized task and motion planning approach for transporting rectangular boxes of varying mass over flat, uphill and downhill terrain. Such a decentralized approach alleviates communication, synchronization and consensus needs and mitigates single point of failure issues. Our approach, called R2P2 or Roles with Rules and Proportional-control Primitive, assigns roles (e.g., push, support and prevent) to robots based on rules cognizant of the mode of manipulation needed (box rotation vs translation); this is followed by either rule-based control or proportional control of robot velocity based on the roles. Each robot is assumed to observe the location and heading of self and the box in executing the role and controls. R2P2 is evaluated with a six-robot team deployed in a simulator built using NVIDIA IsaacSim -- demonstrating generalizability across different surface friction/inclination and box mass scenarios, and better success rate compared to a standard virtual-leader-follower method. R2P2 is also successfully validated with a physical experiment, where it is executed onboard four turtlebots tasked with moving a 1.2 kg box.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The manuscript presents R2P2 (Roles with Rules and Proportional-control Primitive), a decentralized multi-robot approach for collaborative box pushing over flat, uphill, and downhill surfaces with varying friction and box masses. Roles (push, support, prevent) are assigned asynchronously based on rules depending on whether rotation or translation is needed, followed by rule-based or proportional velocity control. Each robot observes its own and the box's location and heading. The method is tested in simulation with six robots in NVIDIA IsaacSim across scenarios, showing better success rates than a virtual-leader-follower baseline, and physically validated with four TurtleBots transporting a 1.2 kg box.

Significance. If the local sensing assumption holds without external infrastructure, R2P2 could provide a communication-free, scalable solution for multi-robot object transport in diverse real-world environments, mitigating single points of failure. The empirical validation across surfaces and masses, plus physical experiments, strengthens its potential applicability in warehouse or disaster scenarios.

major comments (1)
  1. [Abstract] The decentralization claim ('alleviates communication, synchronization and consensus needs') rests on the assumption that 'Each robot is assumed to observe the location and heading of self and the box'. No further detail is given on the sensor model, noise, or implementation in physical experiments (onboard vs. external tracking). This directly impacts whether the reported success-rate advantage over the baseline generalizes to fully decentralized, local-sensing settings.
minor comments (2)
  1. [Abstract] The abstract states 'better success rate' and 'generalizability' but provides no quantitative metrics, error bars, number of trials, or specific success percentages, limiting assessment of the improvement's magnitude and statistical significance.
  2. [Abstract] The physical validation is described only as 'successfully validated' with four TurtleBots and a 1.2 kg box; additional details on surface conditions, success criteria, or failure modes would improve clarity.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback. We address the major comment below and will revise the manuscript to provide the requested clarifications.

read point-by-point responses

  1. Referee: [Abstract] The decentralization claim ('alleviates communication, synchronization and consensus needs') rests on the assumption that 'Each robot is assumed to observe the location and heading of self and the box'. No further detail is given on the sensor model, noise, or implementation in physical experiments (onboard vs. external tracking). This directly impacts whether the reported success-rate advantage over the baseline generalizes to fully decentralized, local-sensing settings.

    Authors: We agree that the manuscript would benefit from additional details on the sensing assumptions to support the decentralization claims. The approach is designed such that each robot requires only its own pose and the box pose, with no inter-robot communication or central coordination needed. In the revised manuscript, we will expand the methods section to describe the sensor model and implementation for both simulation (in NVIDIA IsaacSim) and the physical experiments (where the algorithm executes onboard the TurtleBots), including any provisions for noise or tracking method. This will clarify the conditions under which the success-rate advantages hold in fully local-sensing settings. revision: yes

Circularity Check

0 steps flagged

No circularity; rule-based method with independent empirical validation

full rationale

The paper presents R2P2 as a rule-based assignment of roles (push/support/prevent) followed by proportional velocity control, evaluated via simulation (IsaacSim, 6 robots) and physical TurtleBot experiments (4 robots, 1.2 kg box) against a virtual-leader-follower baseline. No derivation chain, equations, fitted parameters, or self-citations are invoked to produce the central claims; success rates and generalizability are reported as direct experimental outcomes. The method is self-contained against external benchmarks with no reduction of predictions to inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms or invented entities identifiable from the abstract alone.

pith-pipeline@v0.9.1-grok · 5796 in / 974 out tokens · 28076 ms · 2026-06-29T17:42:45.010144+00:00 · methodology

discussion (0)

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