Multi-Object Grasping Protocols

• Multi-object grasping protocols are structured methods that enable robotic systems to pick, manipulate, and transfer multiple objects using discrete rounds and defined performance metrics.
• They integrate techniques like exhaustive pre-grasp sampling, stochastic optimization, and deep learning for reliable grasp synthesis and accurate grasp cardinality estimation.
• Benchmarking metrics such as overall success rate, cycle time, and cost per object provide actionable insights for enhancing hardware design and real-world robotic manipulation.

Multi-object grasping protocols define systematic strategies, algorithmic pipelines, and execution schemes to enable robotic hands and grippers to pick, manipulate, and transfer multiple discrete objects simultaneously or in rapid succession. These protocols span hardware-agnostic theoretical formulations, concrete planning and control methods for specific hardware (e.g., Barrett Hand, parallel-jaw grippers, vacuum arrays), and benchmarking methodologies. Central goals include maximizing throughput (objects-per-transfer), matching target grasp cardinality, and maintaining high reliability under realistic bin or tabletop clutter scenarios.

1. Foundational Protocol Structures

Canonical multi-object grasping protocols organize the robotic task into discrete rounds or atomic actions, each with explicitly defined objectives, stopping criteria, and evaluation metrics. Three widely adopted paradigms—the Only-Pick-Once (OPO), Accurate Pick-Transferring (APT), and Pick-Transferring-All (PTA) protocols—structure the grasping process as follows (Chen et al., 25 Mar 2025):

1. Only-Pick-Once (OPO): The robot attempts to pick exactly $p$ objects from a pile or surface in a single grasp-lift round. Performance is measured by the overall success rate (OSR), picking accuracy (normalized RMSE against $p$), and protocol availability.
2. Accurate Pick-Transferring (APT): Given a global target $N_\text{target}$, the robot repeats OPO rounds (each targeting up to $p$ objects) and transfers the grasped objects to a destination bin, until $N_\text{target}$ is reached. Efficiency is quantified as the cost per object (CGPU), normalized by a single-object picking baseline.
3. Pick-Transferring-All (PTA): The robot repeats OPO and transfer cycles until all objects are cleared from the source bin, measuring the total cycle time, number of necessary rounds, and cumulative efficiency.

These protocols provide a reproducible framework for quantitatively benchmarking multi-object grasping performance across disparate hardware and perception-planning stacks.

2. Algorithmic Techniques for Multi-Object Grasp Synthesis

Multi-object grasping workflows employ a spectrum of algorithmic strategies, including exhaustive pre-grasp sampling, probabilistic and clustering-based selection, machine learning, and stochastic optimization.

Pre-grasp Configuration Sampling and Selection:

For high-DOF hands such as the Barrett Hand, the protocol starts with uniform sampling of the kinematic configuration space—e.g., 9,000 samples over the 4-DOF space (spread, three finger base joints)—to cover the feasible "ready" hand states (Shenoy et al., 2021). Each sampled configuration is evaluated by repeatedly running a Stochastic Flexing Routine (SFR) in simulation to empirically estimate the probability $p_i$ of grasping exactly $i$ objects, forming the Potential Pre-Grasp (PPG) vector. Task-specific clustering and expectation criteria select an optimal configuration. Notable selection strategies:

• Clustered Probability Pre-Grasp (CPPG): Maximizes $p_q$ for a specific target $q$.
• Best Expectation Pre-Grasp (BEPG): Maximizes expected object count via the Average Grasp Potential:

$$\mathrm{AGP}(\theta, O) = \sum_{i=1}^{m} i \cdot p_i.$$

• Maximum Capability Pre-Grasp (MCPG): Selects by maximizing the in-grasp envelope volume $V(\theta)$.

Flexion Synergy Discovery:

Post pre-grasp, end-grasp results are clustered to identify joint vectors (synergies) that optimize success for grasping $i$ objects. Centroids from high-performing clusters are used to bias the hand during execution toward the desired grasp cardinality.

Simultaneous Grasp Planning for Dexterous Hands:

MultiGrasp extends force-closure grasp quality metrics to multiple objects. Contact sets are sampled using a PointNet++-based neural sampler, followed by local optimization (e.g., sequential quadratic programming) to maximize the multi-object $\epsilon$-metric subject to kinematic reachability and collision constraints. Stability is checked via feasible force closure over all object contacts (Li et al., 2023).

Push-based Grouping for Multi-object Grasp Enabling:

Push-MOG introduces a planning layer that computes "fork pushing" sequences, using a parallel-jaw gripper to spatially consolidate objects into clusters aligned with the gripper's aperture, maximizing the expected group size for one-shot grasping (Aeron et al., 2023).

Graph-based and Predictive Protocols:

OPOS uses scene-to-graph conversion, clique-based clustering, geometric fit/ranking, and a CNN predictor to choose clusters and poses that maximize the likelihood of grasping the exact target number of objects in a single pick (Ye et al., 2023).

3. Sequential and Simultaneous Multi-object Grasp Execution

Different hardware and protocol formulations enable one-shot simultaneous grasps, sequential multi-object acquisition without release, or hybrid manipulations.

MDP-based Pick-Transfer Policy Synthesis:

The MOGT protocol formulates multi-object pick-transfer as an MDP with state $s$ (objects transferred), actions as grasp attempts $g_i$, and empirically estimated transition probabilities $p(s'|s, g_i)$. The reward function balances efficient completion and overflow penalties. Value iteration yields an optimal policy $\pi^*(s)$ to minimize transfer rounds. At each step, optimal pre-grasp and flexion synergy are dynamically selected by the policy (Shenoy et al., 2021).

Simultaneous Multi-object Grasping via Co-Optimization:

For planar parallel-jaw grippers, configuration and shape co-optimization jointly solves for object contact locations, gripper pose, and jaw geometry using an augmented Lagrangian framework, ensuring all targets are stably grasped by designed jaws (Jiang et al., 2023).

Sequential Grasping with Partial DoF Freezing:

Recent protocols such as SeqGrasp and SeqMultiGrasp operate by freezing a subset of hand DOFs after each successful object grasp, progressively reducing the available kinematic subspace for subsequent picks (Lu et al., 28 Mar 2025, He et al., 12 Mar 2025). At each step, masked optimization (e.g., MALA sampling) or conditional diffusion models generate stable partial-DoF grasps, merged into a globally feasible multi-object configuration. Simulation and real-robot experiments demonstrate superior success rates over simultaneous methods, especially with three or more objects.

Compliant, Layered, and Suction-based Protocols:

Adaptive soft grippers with mechanically layered origami modules utilize passive deformation and single-DOF actuation to robustly conform to and independently manipulate stacked objects, releasing specific subsets by discrete servo angle transitions—enabling multi-object manipulation without sensing or feedback (Wang et al., 1 Nov 2025). Multi-suction-cup arrays perform affordance convolution, cup-activation decoding, and geometric ranking to maximize the number of objects grasped per cycle (Jiang et al., 2023).

4. Sensing, Estimation, and Closed-Loop Execution

Accurate multi-object protocols integrate tactile, proprioceptive, and learned models for grasp cardinality sensing and closed-loop execution.

• Grasp Volume Calculation: The convex hull of fingertip/palm points in pre-grasp provides an upper bound on graspable object count; packing density is used for cardinality estimation (Chen et al., 2021).
• Tactile Force Regression: Summed normal forces from tactile arrays correlate linearly with the number of enclosed objects, providing real-time feedback before and after lift.
• Data-driven Sensing: Deep learning models (autoencoders + classifiers/regressors) fuse joint, strain, and tactile readings to estimate the post-lift object count, facilitating in-the-pile decision making and lift/re-grasp logic.
• Real-time Classifiers: Voting ensembles over multiple CNN models (detecting nonzero, 1,2,3,$\geq$2 objects) guide the timing of the lift action and in-situ success confirmation (Shenoy et al., 2021).

5. Benchmarking, Evaluation, and Comparative Performance

Standardized protocols and evaluation schemes provide quantitative baselines for multi-object grasping research, allowing for direct cross-comparison among methods and hardware.

• Success Rates and RMSE Metrics: OPO, APT, and PTA protocols record OSR, per-grasp accuracy (RMSE), and efficiency normalized by single-object rates. Notably, parallel grippers achieve ≈97% OSR at $p=2$ but fall off rapidly for $p=4$, whereas dexterous and soft hands maintain moderate success up to $p=3$ (Chen et al., 25 Mar 2025).
• Efficiency Gains: Push-MOG yields a 34% increase in objects-per-trip compared to baselines. MOGT achieves 59% fewer transfers and 58% fewer lifts relative to one-at-a-time pick-transfer (Aeron et al., 2023, Shenoy et al., 2021).
• Sim-to-real Gaps: Protocols incorporating learned policies and perception models demonstrate partial transferability, with performance bottlenecked by grasping model generalization and sensor reliability (He et al., 12 Mar 2025, Yonemaru et al., 12 Feb 2025, Ye et al., 2023).
• Human Baselines: Human operators consistently outperform robots in multi-object pick and clear-all tasks (CGPU_s ≈0.3), leveraging adaptive grouping and in-hand storage strategies (Chen et al., 25 Mar 2025).
• Failure Modes: Primary issues include excessive cardinality variance (declining exact-picking rates for $p>3$), slip and collision among transferred objects, compliance mismatch, and sensor/modeling errors in cluttered scenarios.

6. Practical Considerations, Limitations, and Future Extensions

While advances in multi-object grasping protocols significantly improve throughput and flexibility, salient limitations and open research questions persist:

• Hardware and Scenario Dependence: Tactile-based classifiers require calibrated, high-resolution arrays; transition statistics for MDP policies are object and hand specific.
• Generalizability: Many protocols assume uniform object shapes and sizes. Performance degrades for heterogeneous or deformable objects.
• Computation: Exhaustive pre-grasp sampling and clustering, or offline shape/parameter co-optimization, can be computationally intensive, motivating hybrid learning or real-time pruning approaches.
• Scalability and Integration: Current sequential and simultaneous strategies may not scale beyond four-object grasp without hybridization or dynamic subspace reallocation (Lu et al., 28 Mar 2025).
• Learning-based Methods: Diffusion and imitation learning policies for grouping and grasping require large, high-quality datasets for robust policy transfer; generalization to novel object classes and complex clutter is not guaranteed (Yonemaru et al., 12 Feb 2025).
• Sensorless and Passive Approaches: Mechanical protocols, e.g., adaptive origami grippers, eliminate sensing dependencies but rely on strict geometric and friction constraints for reliable multi-object manipulation (Wang et al., 1 Nov 2025).

Continued progress is expected via more sophisticated perception for instance segmentation in dense clutter, self-supervised learning for group-grasp policies, modular gripper and hand design, and the development of unified benchmarking suites for protocol-level comparison across diverse platforms and object classes.