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arxiv: 1907.02555 · v1 · pith:XNMXYLBUnew · submitted 2019-07-04 · 💻 cs.RO

Object Placement Planning and Optimization for Robot Manipulators

Joshua A. Haustein , Kaiyu Hang , Johannes Stork , Danica Kragic This is my paper

Pith reviewed 2026-05-25 09:05 UTC · model grok-4.3

classification 💻 cs.RO
keywords object placement planningmotion planningrobot manipulatorshierarchical searchsampling-based planningcluttered environmentsdual-arm robotanytime algorithm
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The pith

An anytime algorithm finds reachable stable object placements optimizing a given objective in clutter.

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

The paper addresses the problem of planning motions to place a grasped object in a cluttered environment, where the target pose is not predefined but must be located to meet stability, reachability, and an optimization objective. It proposes an anytime algorithm that couples sampling-based motion planning for the manipulator with a novel hierarchical search over candidate object poses. The search identifies poses satisfying the constraints while optimizing the objective, after which the planner connects the robot to a selected pose. Experiments on a dual-arm robot for two placement objectives demonstrate effectiveness in challenging cluttered scenes. A sympathetic reader would care because classical motion planning assumes a fixed target pose, whereas placement tasks require searching the pose space itself.

Core claim

The paper claims that an anytime algorithm integrating sampling-based motion planning with a novel hierarchical search over candidate object poses can locate collision-free, reachable, and stable placements that optimize a user-given objective, and that this approach succeeds even in challenging cluttered scenarios for two different placement objectives on a dual-arm robot.

What carries the argument

The novel hierarchical search over candidate object poses, which identifies valid placements satisfying stability, reachability, and the optimization objective before the sampling-based planner connects the robot arm to them.

If this is right

  • The method applies directly to dual-arm robots executing placement tasks with different user objectives.
  • The anytime property supports trading off computation time against placement quality in time-critical settings.
  • The approach extends classical motion planning by searching the pose space rather than assuming a fixed target.
  • It remains effective when many candidate poses are invalid due to clutter.

Where Pith is reading between the lines

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

  • The hierarchical search structure could be adapted to online updates if the environment changes during execution.
  • Similar pose-search layers might combine with other sampling-based planners for tasks like assembly or rearrangement.
  • The separation between pose search and arm planning suggests the method could scale to higher-dimensional configuration spaces if the search is further optimized.

Load-bearing premise

The hierarchical search can reliably identify object poses that are collision-free, reachable by the robot, and stable while optimizing the objective, and the sampling-based planner can then connect the robot to those poses.

What would settle it

Running the algorithm on a cluttered scene known to contain valid placements and finding that the hierarchical search either returns no poses or only poses that the motion planner cannot reach without collision or that fail stability checks.

Figures

Figures reproduced from arXiv: 1907.02555 by Danica Kragic, Johannes Stork, Joshua A. Haustein, Kaiyu Hang.

Figure 1
Figure 1. Figure 1: Our algorithm computes placements for objects as well as corresponding approach motions in cluttered environments. In addition, it optimizes a user-specified objective for the placement pose. In the top row are example placements produced by our algorithm for a wine glass and toy table (green) under the objective to maximize clearance from other objects. In the bottom row a small and a large crayons box (g… view at source ↗
Figure 2
Figure 2. Figure 2: Our approach consists of two stages. In a pre-processing stage we first extract placement regions and faces that help locating us stable object poses. In the optimization stage a sampling algorithm is employed to locate kinematically reachable and collision-free stable placement poses. These are provided to a motion algorithm to verify path-reachability and construct an approach motion. Subsequently a loca… view at source ↗
Figure 3
Figure 3. Figure 3: Placement faces and regions. Left: The Stanford bunny model is shown with its convex hull and two of the hull’s faces are highlighted (purple and green). By projecting the center of mass (red) along the faces’ normals, we can determine which face supports a stable horizontal placement. For faces f ∈ F that support a placement, we refer by v(f) to its vertices and by f T o to the transformation matrix from … view at source ↗
Figure 4
Figure 4. Figure 4: The AFR hierarchy constitutes of two different parts. On the first three level, the hierarchy represents choices of an arm a ∈ A, a placement face f ∈ F and a region r ∈ R. On the level at greater depths, the hierarchy recursively subdivides the region r and the range of orientations [ ˇθ, ˆθ) within a pose set Sˆ(r, f) C. Sampling Kinematically Feasible Placements The function SAMPLEGOALS needs to solve a… view at source ↗
Figure 5
Figure 5. Figure 5: Experiments scenes and optimization performance of different instances of our algorithm. The plots in (d) - (i) show the mean relative objective achieved by each algorithm as a function of planning time. The plots show the average optimization performance across all test objects. In order to make the objective values comparable, we normalize the achieved objective values for each scene and object into the … view at source ↗
read the original abstract

We address the problem of motion planning for a robotic manipulator with the task to place a grasped object in a cluttered environment. In this task, we need to locate a collision-free pose for the object that a) facilitates the stable placement of the object, b) is reachable by the robot manipulator and c) optimizes a user-given placement objective. Because of the placement objective, this problem is more challenging than classical motion planning where the target pose is defined from the start. To solve this task, we propose an anytime algorithm that integrates sampling-based motion planning for the robot manipulator with a novel hierarchical search for suitable placement poses. We evaluate our approach on a dual-arm robot for two different placement objectives, and observe its effectiveness even in challenging scenarios.

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 / 0 minor

Summary. The paper claims to solve object placement for a grasped item in clutter by locating a collision-free, reachable, stable pose that optimizes a user-specified objective. It proposes an anytime algorithm that couples sampling-based robot motion planning with a novel hierarchical search over candidate object poses, and reports that the method is effective on a dual-arm robot for two placement objectives even in challenging cluttered scenes.

Significance. If the empirical outcomes are reproducible and quantitatively supported, the integration of standard sampling-based planning with an additional hierarchical search layer could provide a practical, anytime-capable solution for manipulation tasks where the target pose is not known a priori.

major comments (1)
  1. [Abstract / Evaluation] Abstract and experimental description: the central empirical claim of effectiveness on a dual-arm robot is asserted without quantitative metrics, baselines, failure rates, or implementation details in the supplied text. This directly affects verifiability of the weakest assumption (that the hierarchical search reliably returns valid optimizing poses) and is load-bearing for the paper's main contribution.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their review and for highlighting the need for stronger quantitative support. We address the major comment below and will revise the manuscript to improve verifiability of the empirical claims.

read point-by-point responses

  1. Referee: [Abstract / Evaluation] Abstract and experimental description: the central empirical claim of effectiveness on a dual-arm robot is asserted without quantitative metrics, baselines, failure rates, or implementation details in the supplied text. This directly affects verifiability of the weakest assumption (that the hierarchical search reliably returns valid optimizing poses) and is load-bearing for the paper's main contribution.

    Authors: We agree that the abstract and evaluation sections would benefit from additional quantitative detail to support the claims of effectiveness. In the revised version we will expand the abstract with key metrics (e.g., success rates, planning times, and objective values achieved) and will add explicit comparisons to baselines, failure rates, and implementation parameters in the experimental section. These additions will directly address the reliability of the hierarchical search component and improve verifiability without altering the core technical contribution. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper describes an empirical integration of sampling-based motion planning with a hierarchical search over object poses for placement tasks. No equations, parameter fits, or formal derivations are present that could reduce to inputs by construction. The approach relies on standard techniques plus a new search layer, with effectiveness shown via experiments rather than self-referential definitions or load-bearing self-citations. The central claim remains independent of any circular reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no identifiable free parameters, axioms, or invented entities; the approach relies on standard assumptions of sampling-based planning and environment modeling.

pith-pipeline@v0.9.0 · 5655 in / 965 out tokens · 27228 ms · 2026-05-25T09:05:30.028778+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

27 extracted references · 27 canonical work pages · 1 internal anchor

  1. [1]

    For instance, an object may be placed flat on a horizontal surface, leaned against a wall, placed on a hook, or laid on top of other objects

    It needs to identify suitable locations that afford plac- ing. For instance, an object may be placed flat on a horizontal surface, leaned against a wall, placed on a hook, or laid on top of other objects. Determining how and where a particular object can be placed, requires analysis of both the environment’s and the object’s physical properties

    work page

  2. [2]

    Placing requires the robot to move close to obstacles, which make it difficult to compute collision-free arm configu- rations reaching a placement

    It needs to be able to reach the placement. Placing requires the robot to move close to obstacles, which make it difficult to compute collision-free arm configu- rations reaching a placement. In addition, the obstacles render planning an approach motion computationally expensive

    work page

  3. [3]

    Object Placement Planning and Optimization for Robot Manipulators

    Not all placements are equally desirable. For many tasks, there exists an objective such as stability, human- preference on location or clearance from other obsta- cles, that is to be maximized. 1 Division of Robotics, Perception and Learning (RPL), CAS, CSC, KTH Royal Institute of Technology, Stockholm, Sweden, E-mail: haustein, dani@kth.se 2GRAB Lab, Ya...

    work page internal anchor Pith review Pith/arXiv arXiv 1907

  4. [4]

    In addition, to compute an arm configuration reaching a sampled pose, we need to select an arm a ∈ A

    AFR-Hierarchy: Sampling a pose from ˆS involves choosing a placement contact region r ∈ R and a placement face f ∈ F . In addition, to compute an arm configuration reaching a sampled pose, we need to select an arm a ∈ A . While there is an overlap of the poses that each arm can reach, some may be more easily reached by one than the other. Whether a particu...

    work page

  5. [5]

    (2) we exploit the afore- mentioned correlation and employ a Monte Carlo Tree search (MCTS) [18]-based algorithm for sampling

    Monte Carlo Tree Search-based Goal Sampling: To obtain samples that satisfy Eq. (2) we exploit the afore- mentioned correlation and employ a Monte Carlo Tree search (MCTS) [18]-based algorithm for sampling. The algo- rithm is shown in Algorithm 2 and Algorithm 3, and uses the AFR-hierarchy to produce the desired samples. Algorithm 2 is the SAMPLE GOAL pro...

    work page

  6. [6]

    Robotic grasping and contact: A review,

    A. Bicchi and V . Kumar, “Robotic grasping and contact: A review,” in ICRA, 2000

    work page 2000

  7. [7]

    Data-driven grasp synthesis – a survey,

    J. Bohg, A. Morales, T. Asfour, and D. Kragic, “Data-driven grasp synthesis – a survey,” TRO, vol. 30, no. 2, 2014

    work page 2014

  8. [8]

    Grasp quality measures: review and perfor- mance,

    M. Roa and R. Su ´arez, “Grasp quality measures: review and perfor- mance,” Auton. Robots, vol. 38, no. 1, 2015

    work page 2015

  9. [9]

    Optimal, sampling-based manipulation planning,

    P. S. Schmitt, W. Neubauer, W. Feiten, K. M. Wurm, G. V . Wichert, and W. Burgard, “Optimal, sampling-based manipulation planning,” in ICRA, May 2017

    work page 2017

  10. [10]

    Closed-chain manipulation of large objects by multi-arm robotic systems,

    Z. Xian, P. Lertkultanon, and Q. Pham, “Closed-chain manipulation of large objects by multi-arm robotic systems,” IEEE RA-L , vol. 2, no. 4, pp. 1832–1839, Oct 2017

    work page 2017

  11. [11]

    FFRob: Leverag- ing symbolic planning for efficient task and motion planning,

    C. R. Garrett, T. Lozano-P ´erez, and L. P. Kaelbling, “FFRob: Leverag- ing symbolic planning for efficient task and motion planning,” IJRR, vol. 37, no. 1, pp. 104–136, 2018

    work page 2018

  12. [12]

    A regrasp planning component for object reorientation,

    W. Wan, H. Igawa, K. Harada, H. Onda, K. Nagata, and N. Yamanobe, “A regrasp planning component for object reorientation,” Autonomous Robots, pp. 1–15, jul 2018

    work page 2018

  13. [13]

    A certified-complete bimanual ma- nipulation planner,

    P. Lertkultanon and Q. Pham, “A certified-complete bimanual ma- nipulation planner,” IEEE Transactions on Automation Science and Engineering, vol. 15, no. 3, pp. 1355–1368, July 2018

    work page 2018

  14. [14]

    Perceiving clutter and surfaces for object placement in indoor environments,

    M. J. Schuster, J. Okerman, H. Nguyen, J. M. Rehg, and C. C. Kemp, “Perceiving clutter and surfaces for object placement in indoor environments,” in Humanoids, Dec 2010, pp. 152–159

    work page 2010

  15. [15]

    Vali- dating an object placement planner for robotic pick-and-place tasks,

    K. Harada, T. Tsuji, K. Nagata, N. Yamanobe, and H. Onda, “Vali- dating an object placement planner for robotic pick-and-place tasks,” Robotics and Autonomous Systems , vol. 62, no. 10, pp. 1463 – 1477, 2014

    work page 2014

  16. [16]

    Learning to place new objects in a scene,

    Y . Jiang, M. Lim, C. Zheng, and A. Saxena, “Learning to place new objects in a scene,” IJRR, vol. 31, no. 9, pp. 1021–1043, 2012

    work page 2012

  17. [17]

    Integrating motion and hierarchical fingertip grasp planning,

    J. A. Haustein, K. Hang, and D. Kragic, “Integrating motion and hierarchical fingertip grasp planning,” in ICRA, May 2017, pp. 3439– 3446

    work page 2017

  18. [18]

    Path planning for grasping operations using an adaptive pca-based sampling method,

    J. Rosell, R. Su ´arez, and A. P ´erez, “Path planning for grasping operations using an adaptive pca-based sampling method,” Auton. Robots, 2013

    work page 2013

  19. [19]

    Grasp planning in complex scenes,

    D. Berenson, R. Diankov, K. Nishiwaki, S. Kagami, and J. Kuffner, “Grasp planning in complex scenes,” in Humanoids, 2007

    work page 2007

  20. [20]

    Simultaneous grasp and motion planning: Humanoid robot armar-iii,

    N. Vahrenkamp, T. Asfour, and R. Dillmann, “Simultaneous grasp and motion planning: Humanoid robot armar-iii,” IEEE Robotics Automation Magazine, 2012

    work page 2012

  21. [21]

    Integrated grasp and motion planning using independent contact regions,

    J. Fontanals, B. A. Dang-Vu, O. Porges, J. Rosell, and M. A. Roa, “Integrated grasp and motion planning using independent contact regions,” in Humanoids, Nov 2014

    work page 2014

  22. [22]

    Sampling-based robot motion planning: A review,

    M. Elbanhawi and M. Simic, “Sampling-based robot motion planning: A review,” IEEE Access, vol. 2, pp. 56–77, 2014

    work page 2014

  23. [23]

    A survey of monte carlo tree search methods,

    C. B. Browne, E. Powley, D. Whitehouse, S. M. Lucas, P. I. Cowling, P. Rohlfshagen, S. Tavener, D. Perez, S. Samothrakis, and S. Colton, “A survey of monte carlo tree search methods,” IEEE Transactions on Computational Intelligence and AI in Games , vol. 4, no. 1, pp. 1–43, March 2012

    work page 2012

  24. [24]

    Finite-time analysis of the multiarmed bandit problem,

    P. Auer, N. Cesa-Bianchi, and P. Fischer, “Finite-time analysis of the multiarmed bandit problem,” Machine Learning , vol. 47, no. 2, pp. 235–256, May 2002

    work page 2002

  25. [25]

    The Open Motion Planning Library,

    I. A. S ¸ucan, M. Moll, and L. E. Kavraki, “The Open Motion Planning Library,” IEEE Robotics & Automation Magazine , vol. 19, no. 4, pp. 72–82, December 2012, http://ompl.kavrakilab.org

    work page 2012

  26. [26]

    Rrt-connect: An efficient approach to single-query path planning,

    J. J. Kuffner and S. M. LaValle, “Rrt-connect: An efficient approach to single-query path planning,” in ICRA, vol. 2, April 2000, pp. 995– 1001 vol.2

    work page 2000

  27. [27]

    Automated construction of robotic manipulation pro- grams,

    R. Diankov, “Automated construction of robotic manipulation pro- grams,” Ph.D. dissertation, Carnegie Mellon University, Robotics Institute, 2010

    work page 2010