Gripper-Aware Automatic Dense Packing of Irregular Objects

Tianhao Qin; Connor McCann; Berk Calli; Jing Xiao

cs.ROSep 18, 2026

Gripper-Aware Automatic Dense Packing of Irregular Objects

Authors: Tianhao Qin, Connor McCann, Berk Calli, Jing Xiao

Organizations: Robotics Engineering Department Worcester Polytechnic Institute, Worcester, MA 01609, USA

Abstract

Automatic dense packing is widely desired in warehouse operations but remains a fundamental challenge in robotic manipulation. Existing work on irregular-object packing largely targets simulation with idealized contact, treating the object as an isolated rigid body. The gripper often enters as a discrete, post-hoc feasibility check, if considered at all, and the perception and contact drift accumulated during execution are not addressed. We present a closed-loop pipeline that integrates perception, gripper-aware placement optimization, and force-guided execution on a real manipulator. The optimizer represents the object together with the gripper as a single composite body of hierarchical sphere trees. It searches over five degrees of freedom on a GPU within a CMA-ES framework, with the vertical coordinate grounded analytically against the current heightmap. During execution, a force-monitored vertical descent stops on first contact. A post-release consolidation push then closes the residual lateral clearance that gripper-aware planning leaves behind. The container is re-perceived between placements so that drift does not accumulate. We validate the system on a Franka Emika Panda robot packing a 3D-printed set of flat, curved, and concave objects, and a YCB object subset. An ablation study isolates the contribution of gripper-aware optimization, the consolidation push, and mesh-derived geometry to end-to-end success, achieved density, and computational cost. We further benchmark against the heightmap-minimization method as a baseline representative of prior irregular-object packing work.

Figures & tables

Figure 1: Dense packing pipeline. Perception detects the next target object and scans the container, Planning composes the object with its grasp and searches over placements with CMA-ES, and Execution picks, descends with force/torque monitoring, and consolidates eligible placements before looping back.

Term	Vol.	Cent.	Pack.	Height	Front. †	Coll.	Bounds	Orient.
Symbol	$\hat{f}_{\mathrm{vol}}$	$\hat{f}_{\mathrm{cen}}$	$\hat{f}_{\mathrm{pck}}$	$\hat{f}_{\mathrm{hgt}}$	$\hat{f}_{\mathrm{fro}}$	$\hat{f}_{\mathrm{col}}$	$\hat{f}_{\mathrm{bnd}}$	$\hat{f}_{\mathrm{ori}}$
Weight	2.0	2.0	2.0	10 / 5	10	50	50	50 / 4
Point set	O	O	O	O	O	O+G	O+G	G

Table 1: Optimization configuration: objective function weights with point-set assignments, and CMA-ES parameters.

Panel of Figure 2 — Figure 2: Hardware platform and object set. $\mathbf{Left}$ : (a) Intel RealSense D415 and D455 depth cameras mounted on opposite sides of the workspace, (b) 7-DoF Franka Emika Panda Haddadin et al. (2022) , (c) ATI wrist force/torque sensor, (d) 3D-printed soft parallel fin-ray gripper, (e) Container, (f) Staging area with the seven-object set. $\mathbf{Right}$ : the eight-object YCB subset used to test generalization.

Sequence	Object ordering
Optimized	support, duck, duck, vase, vase, L, L
Random 1	vase, L, vase, L, duck, duck, support
Random 2	duck, vase, L, support, L, vase, duck
YCB	mug, tomato soup can, banana, potted meat can, pudding box, gelatin box, pear, orange

Table 2: Test sequences (five trials each).

ID	Ablations	Hypothesis tested
A0	Full system	Reference for paired comparison.
A1	Prism-like geometry	Planform-extrusion proxy Cao and Xiao (2026) cannot pack as dense as using the irregular shapes directly.
A2	No consolidation push	Post-release push contributes measurably to final density.
A3	No gripper-aware optimization	Omitting the gripper from the composite body causes collision.

Table 3: Ablation variants, run on the Optimized sequence.

Metric	Optimized	Random 1	Random 2	Overall
Space utilization (%)	$\mathbf{95.59\boldsymbol{\pm}6.06}$	$81.37\pm 0.02$	$75.19\pm 5.66$	$84.05\pm 9.89$
Objects packed	$\mathbf{6.60\boldsymbol{\pm}0.55}$	$6.00\pm 0.00$	$5.40\pm 0.55$	$6.00\pm 0.65$
End-to-end success	$\mathbf{5/5}$	$4/5$	$\mathbf{5/5}$	$14/15$
Per-placement success	$\mathbf{35/35}$	$34/35$	$\mathbf{35/35}$	$104/105$
CMA wall time (s/obj)	$3.56\pm 0.20$	$3.32\pm 0.21$	$\mathbf{3.19\boldsymbol{\pm}0.11}$	$3.34\pm 0.22$

Table 4: Average system performance of three test sequences (five trials each).

Figure 3: Three final packing results (out of five) for each sequence. The Optimized sequence produces visibly flatter and tighter configurations, while the random orderings leave larger residual cavities as small objects are committed early.

	Space utilization (%)	Objects packed	CMA wall time (s/obj)
YCB subset	$82.45\pm 10.36$	$6.60\pm 0.89$	$3.00\pm 0.46$

Table 5: Average system performance on the YCB objects (five trials each).

Figure 4: Final packing results for all five trials of the YCB sequence.

Metric	A0 (Full)	A1 (Prism-like)	A2 (No-push)	A3 (No-gripper)
Space utilization (%)	$\mathbf{95.59\boldsymbol{\pm}6.06}$	$80.00\pm 9.50$	$70.62\pm 12.43$	$26.09\pm 21.23$
Objects packed	$\mathbf{6.60\boldsymbol{\pm}0.55}$	$5.20\pm 0.84$	$5.20\pm 0.84$	$1.40\pm 1.14$
End-to-end success	$\mathbf{5/5}$	$\mathbf{5/5}$	$\mathbf{5/5}$	$0/5$
Per-placement success	$\mathbf{35/35}$	$\mathbf{35/35}$	$\mathbf{35/35}$	$7/35$
CMA wall time (s/obj)	$3.56\pm 0.20$	$\mathbf{1.87\boldsymbol{\pm}0.12}$	$3.30\pm 0.28$	$2.69\pm 0.39$

Table 6: Paired ablation on the Optimized sequence.

Figure 5: Three final packing results (out of five) for each ablation variant on the Optimized sequence.

Figure 6: Comparison of space utilization and objects packed. (a) The left three bars correspond to the sequential packing tests. (b) The right three bars correspond to the ablation variants on the Optimized sequence. Bars show the mean over five trials per configuration with error bars indicating one standard deviation.

Metric	Optimized	HM_Low_Resolution	HM_High_Resolution
Space utilization (%)	$\mathbf{95.59\boldsymbol{\pm}6.06}$	$77.66\pm 8.33$	$82.35\pm 6.06$
Objects packed	$\mathbf{6.60\boldsymbol{\pm}0.55}$	$5.00\pm 0.71$	$5.40\pm 0.55$

Table 7: Full system (Optimized sequence) vs. Wang and Hauser Wang and Hauser (2022) heightmap-minimization (HM) baseline, with two search budgets: HM_Low_Resolution (10 mm XY step,

\Delta r=\pi/4

, their Sec. VI setting) and HM_High_Resolution (5 mm step, 16 yaws, a matched search budget). Five trials each.

Figure 7: Final packing results for the HM baseline on the Optimized sequence.

Robotic Manipulation

Robotic manipulation studies how robots perceive, plan, and control physical interactions to grasp, move, assemble, and use objects.

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Robotic RL Robotics Robotic Manipulation Learning from Demonstration

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Packing objects efficiently is a fundamental problem in logistics, warehouse automation, and robotics. When dealing with highly diverse 3D objects (household or grocery items), closed-form solutions are infeasible, and heuristic or model-free Reinforcement Learning~(RL) methods tend to focus solely on geometric optimization, relying on exhaustive searches of the discretized solution space. This leads to long training times (for pure RL) and high latency (heuristics), limited transferability to robotic scenarios, and ultimately ignores object characteristics (fragility, deformability) and human preferences. We propose HERB, a human-augmented RL framework for packing irregular objects, the first to explore the potential of learning from human demonstrations to solve this complex task. It leverages human demonstrations of packing strategies, which inherently exhibit latent factors such as space optimization, stability, and object properties that are difficult to model explicitly. The human-expert data is combined with RL exploration to provide the placement of each object inside the container. Experimental results show that our method outperforms heuristic, purely RL-based, and imitation learning approaches in packing efficiency and latency. Qualitative results highlight that our packing strategy produces more stable, human-like arrangements, which we expect to be more appropriate and widely accepted. Finally, we demonstrate the real-world feasibility of our method on a robotic system.

Abstract

Robotic Manipulation

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