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arxiv: 2602.12095 · v3 · submitted 2026-02-12 · 💻 cs.RO

Pack it in: Packing into Partially Filled Containers Through Contact

David Russell , Zisong Xu , Maximo A. Roa , Mehmet Dogar This is my paper

Pith reviewed 2026-05-16 02:53 UTC · model grok-4.3

classification 💻 cs.RO
keywords containersitemspackingwarehousebin-packingfilledobjectoften
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The pith

A contact-based multi-object trajectory optimizer inside a model predictive controller, paired with physics-aware perception, enables packing new items into partially filled containers by exploiting purposeful interactions with existing objects.

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

Warehouse robots often face bins that are already half full with items in random positions from earlier moves. Instead of requiring an empty bin or forcing a full rearrangement, this approach lets the robot gently push against existing objects during placement. It uses a physics-aware camera system to guess where hidden items are even when the view is blocked. Then a special planner calculates a path for the new item that includes touching and shifting other objects just enough to make room. The planner runs inside a model predictive controller that repeatedly updates its plan as the robot moves. This turns a hard packing problem into one where contact is allowed and useful rather than avoided.

Core claim

This paper presents a contact-aware packing approach that exploits purposeful interactions with previously placed objects to create free space and enable successful placement of new items. This is achieved by using a contact-based multi-object trajectory optimizer within a model predictive controller, integrated with a physics-aware perception system that estimates object poses even during inevitable occlusions.

Load-bearing premise

The physics-aware perception system can reliably estimate object poses and shapes even under heavy occlusions, and the contact-based optimizer can generate physically feasible trajectories that do not damage objects or violate container boundaries.

Figures

Figures reproduced from arXiv: 2602.12095 by David Russell, Maximo A. Roa, Mehmet Dogar, Zisong Xu.

Figure 1
Figure 1. Figure 1: Snapshots of the proposed packing through contact system oper [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Control system architecture for MPC on real robotic hardware. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Two visualisations of the same packing scene with different sampled [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Overhead views of 5 of the 40 packing scenes used during hardware [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Box plots of the metrics defined in Eqs. 7–9 for PackItIn trials. [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
read the original abstract

The automation of warehouse operations is crucial for improving productivity and reducing human exposure to hazardous environments. One operation frequently performed in warehouses is bin-packing where items need to be placed into containers, either for delivery to a customer, or for temporary storage in the warehouse. Whilst prior bin-packing works have largely been focused on packing items into empty containers and have adopted collision-free strategies, it is often the case that containers will already be partially filled with items, often in suboptimal arrangements due to transportation about a warehouse. This paper presents a contact-aware packing approach that exploits purposeful interactions with previously placed objects to create free space and enable successful placement of new items. This is achieved by using a contact-based multi-object trajectory optimizer within a model predictive controller, integrated with a physics-aware perception system that estimates object poses even during inevitable occlusions, and a method that suggests physically-feasible locations to place the object inside the container.

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

0 major / 2 minor

Summary. The paper presents a contact-aware packing approach for partially filled containers that exploits purposeful interactions with previously placed objects to create free space. It integrates a contact-based multi-object trajectory optimizer within a model predictive controller, a physics-aware perception system for pose estimation under occlusions, and a method for suggesting physically feasible placement locations. Validation is provided through simulation and hardware experiments demonstrating successful packing sequences.

Significance. This work is significant for advancing robotic bin-packing beyond collision-free methods for empty containers to realistic scenarios with partial fills and suboptimal arrangements. The integration of MPC with complementarity constraints for contacts and physics-informed perception represents a practical contribution to warehouse automation if the non-damaging contact assumptions hold in practice.

minor comments (2)
  1. [Abstract] The abstract describes the integration of optimizer, controller, and perception but provides no quantitative results, error analysis, or validation metrics; adding a brief summary of success rates or key performance numbers would strengthen the summary.
  2. [Perception] The perception pipeline fuses partial point clouds with a rigid-body dynamics prior; the exact weighting or filtering equations should be stated explicitly to allow reproduction.

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The manuscript describes a novel integration of a contact-based multi-object trajectory optimizer inside an MPC, a physics-aware perception pipeline for occluded pose estimation, and a feasibility-aware placement suggestion method. No equations, fitted parameters, or self-citations are presented that reduce any claimed prediction or result to the inputs by construction. The central claims rest on the explicit formulation of complementarity constraints for contacts and the fusion of partial point clouds with rigid-body priors; these components are introduced as independent engineering contributions rather than derived from prior fitted quantities or self-referential definitions within the paper. The derivation chain is therefore self-contained against external benchmarks and does not exhibit any of the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no explicit free parameters, axioms, or invented entities; the central claim implicitly assumes accurate occluded pose estimation and safe contact dynamics without stating their derivation or validation.

pith-pipeline@v0.9.0 · 5459 in / 1073 out tokens · 80548 ms · 2026-05-16T02:53:50.430507+00:00 · methodology

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Reference graph

Works this paper leans on

22 extracted references · 22 canonical work pages

  1. [1]

    Stow: Robotic packing of items into fabric pods,

    N. Hudson, J. Hooks, R. Warrier, C. Salisbury, R. Hartley, K. Kumar, B. Chandrashekhar, P. Birkmeyer, B. Tang, M. Frostet al., “Stow: Robotic packing of items into fabric pods,”arXiv, 2025

    work page 2025

  2. [2]

    Stable bin packing of non-convex 3d objects with a robot manipulator,

    F. Wang and K. Hauser, “Stable bin packing of non-convex 3d objects with a robot manipulator,” inICRA. IEEE, 2019, pp. 8698–8704

    work page 2019

  3. [3]

    Dense robotic packing of irregular and novel 3d objects,

    ——, “Dense robotic packing of irregular and novel 3d objects,”IEEE Transactions on Robotics, vol. 38, no. 2, pp. 1160–1173, 2021

    work page 2021

  4. [4]

    Jam- packer: An efficient and reliable robotic bin packing system for cuboid objects,

    M. Agarwal, S. Biswas, C. Sarkar, S. Paul, and H. S. Paul, “Jam- packer: An efficient and reliable robotic bin packing system for cuboid objects,”IEEE RA-L, vol. 6, no. 2, pp. 319–326, 2020

    work page 2020

  5. [5]

    Towards reliable robot packing system based on deep reinforcement learning,

    H. Xiong, K. Ding, W. Ding, J. Peng, and J. Xu, “Towards reliable robot packing system based on deep reinforcement learning,”Ad- vanced Engineering Informatics, vol. 57, p. 102028, 2023

    work page 2023

  6. [6]

    On-line three-dimensional packing problems: A review of off-line and on-line solution approaches,

    S. Ali, A. G. a. Ramos, M. A. Carravilla, and J. F. Oliveira, “On-line three-dimensional packing problems: A review of off-line and on-line solution approaches,”Comput. Ind. Eng., vol. 168, no. C, 2022

    work page 2022

  7. [7]

    Object rearrangement with nested nonpre- hensile manipulation actions,

    C. Song and A. Boularias, “Object rearrangement with nested nonpre- hensile manipulation actions,” inIEEE/RSJ IROS. IEEE, 2019

    work page 2019

  8. [8]

    Tight robot packing in the real world: A complete manipulation pipeline with robust primitives,

    R. Shome, W. N. Tang, C. Song, C. Mitash, H. Kourtev, J. Yu, A. Boularias, and K. E. Bekris, “Tight robot packing in the real world: A complete manipulation pipeline with robust primitives,”arXiv, 2021

    work page 2021

  9. [9]

    Iterative linear quadratic regulator design for nonlinear biological movement systems,

    W. Li and E. Todorov, “Iterative linear quadratic regulator design for nonlinear biological movement systems,” inFirst International Con- ference on Informatics in Control, Automation and Robotics, vol. 2. SciTePress, 2004, pp. 222–229

    work page 2004

  10. [10]

    Synthesis and stabilization of com- plex behaviors through online trajectory optimization,

    Y . Tassa, T. Erez, and E. Todorov, “Synthesis and stabilization of com- plex behaviors through online trajectory optimization,” inIEEE/RSJ IROS. IEEE, 2012, pp. 4906–4913

    work page 2012

  11. [11]

    Track- ing and control of multiple objects during non-prehensile manipulation in clutter,

    Z. Xu, R. Papallas, J. Modisett, M. Billeter, and M. R. Dogar, “Track- ing and control of multiple objects during non-prehensile manipulation in clutter,”IEEE Transactions on Robotics, 2025

    work page 2025

  12. [12]

    Robotic tight packaging using a hybrid gripper with variable stiffness,

    M. Moroni, A. E. H. Martin, L. Klüpfel, A. M. Sundaram, W. Friedl, F. Braghin, and M. A. Roa, “Robotic tight packaging using a hybrid gripper with variable stiffness,” inAnnual Conference Towards Au- tonomous Robotic Systems. Springer, 2024, pp. 313–326

    work page 2024

  13. [13]

    Physics-based tra- jectory optimization for grasping in cluttered environments,

    N. Kitaev, I. Mordatch, S. Patil, and P. Abbeel, “Physics-based tra- jectory optimization for grasping in cluttered environments,” inICRA. IEEE, 2015, pp. 3102–3109

    work page 2015

  14. [14]

    Online replanning with human-in-the-loop for non-prehensile manipulation in clutter—a tra- jectory optimization based approach,

    R. Papallas, A. G. Cohn, and M. R. Dogar, “Online replanning with human-in-the-loop for non-prehensile manipulation in clutter—a tra- jectory optimization based approach,”IEEE RA-L, 2020

    work page 2020

  15. [15]

    Real-time online re-planning for grasping under clutter and uncertainty,

    W. C. Agboh and M. R. Dogar, “Real-time online re-planning for grasping under clutter and uncertainty,” inHumanoids. IEEE, 2018

    work page 2018

  16. [16]

    The three-dimensional bin packing problem,

    S. Martello, D. Pisinger, and D. Vigo, “The three-dimensional bin packing problem,”Operations research, vol. 48, no. 2, 2000

    work page 2000

  17. [17]

    First fit bin packing: A tight analysis,

    G. Dósa and J. Sgall, “First fit bin packing: A tight analysis,” in30th STACS. Schloss Dagstuhl–Leibniz-Zentrum fuer Informatik, 2013

    work page 2013

  18. [18]

    Two natural heuris- tics for 3d packing with practical loading constraints,

    L. Wang, S. Guo, S. Chen, W. Zhu, and A. Lim, “Two natural heuris- tics for 3d packing with practical loading constraints,” inPRICAI. Springer, 2010

    work page 2010

  19. [19]

    Mujoco: A physics engine for model-based control,

    E. Todorov, T. Erez, and Y . Tassa, “Mujoco: A physics engine for model-based control,” inIEEE/RSJ IROS. IEEE, 2012

    work page 2012

  20. [20]

    Predictive sampling: Real-time behaviour synthesis with mujoco,

    T. Howell, N. Gileadi, S. Tunyasuvunakool, K. Zakka, T. Erez, and Y . Tassa, “Predictive sampling: Real-time behaviour synthesis with mujoco,”arXiv, 2022

    work page 2022

  21. [21]

    Adaptive approximation of dynamics gradients via interpolation to speed up trajectory optimisa- tion,

    D. Russell, R. Papallas, and M. Dogar, “Adaptive approximation of dynamics gradients via interpolation to speed up trajectory optimisa- tion,” inICRA. IEEE, 2023

    work page 2023

  22. [22]

    Online state vector reduction during model predictive control with gradient-based trajectory optimisation,

    ——, “Online state vector reduction during model predictive control with gradient-based trajectory optimisation,” inSPAR. Springer, 2024

    work page 2024