arxiv: 2604.25126 · v1 · submitted 2026-04-28 · 💻 cs.RO

Recognition: unknown

HANDFUL: Sequential Grasp-Conditioned Dexterous Manipulation with Resource Awareness

Ethan Foong , Yunshuang Li , Hao Jiang , Gaurav S. Sukhatme , Daniel Seita

Authors on Pith no claims yet

Pith reviewed 2026-05-07 16:14 UTC · model grok-4.3

classification 💻 cs.RO

keywords dexterous manipulationsequential tasksgrasp planningresource awarenessfinger-level rewardscurriculum learningsimulation benchmarkmultifunctional manipulation

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The pith

Treating fingers as a limited resource during initial grasps improves success on follow-up dexterous manipulation tasks.

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

The paper establishes that sequential dexterous tasks, where a robot must grasp an object and then execute a distinct second action like pushing or pressing while keeping the grasp, require planning initial grasps that conserve fingers rather than maximizing only the first action. A sympathetic reader would care because most existing dexterous manipulation work targets isolated single-skill episodes, yet practical multifunctional use demands maintaining object control across steps without exhausting hand resources. The authors introduce a framework that adds finger-level contact rewards to promote such resource-aware grasps and then trains policies on them through curriculum learning. They support this with a new benchmark containing multiple grasp-conditioned second-subtask objectives and show higher second-subtask success and robustness than a greedy baseline that ignores future finger needs. Real-robot validation on a physical hand further indicates the principle transfers beyond simulation.

Core claim

By modeling finger usage as a limited resource and applying finger-level contact rewards during grasp learning, the framework produces initial grasps that preserve both stability and finger availability; when these grasps are selected via curriculum-based policy learning for downstream subtasks, second-subtask success rates and robustness rise compared with baselines that greedily optimize only the initial grasp.

What carries the argument

Finger-level contact rewards that treat individual fingers as scarce resources during grasp optimization, combined with curriculum-based policy selection to favor grasps compatible with a second subtask.

If this is right

Prioritizing resource conservation in the initial grasp directly raises the probability of completing the second subtask without dropping the object.
Curriculum learning on resource-aware grasps produces policies that generalize better across pushing, pulling, and pressing objectives under the same grasp-conditioned setup.
The same principle yields measurable robustness gains in simulation and transfers to physical execution on a multi-fingered hand.
A shared benchmark of sequential tasks provides a concrete testbed for comparing grasp strategies that account for future finger availability.

Where Pith is reading between the lines

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

Longer task sequences beyond two subtasks would likely require extending the reward structure to track remaining finger budgets across multiple steps.
The approach could be combined with perception modules so that grasp selection adapts online to object geometry variations not seen in simulation.
Explicit resource modeling may reduce the need for frequent re-grasping in real deployments where finger fatigue or contact drift occurs.

Load-bearing premise

Finger-level contact rewards during the first grasp will consistently yield holds that remain stable while leaving the needed fingers free for the second subtask, even across different objects and real-world conditions.

What would settle it

A controlled comparison on the benchmark tasks in which the resource-aware method shows no gain or a loss in second-subtask success rate relative to the greedy initial-grasp baseline, under identical training budgets and object sets.

Figures

Figures reproduced from arXiv: 2604.25126 by Daniel Seita, Ethan Foong, Gaurav S. Sukhatme, Hao Jiang, Yunshuang Li.

**Figure 1.** Figure 1: HANDFUL enables sequential dexterous manipulation by learning resource-aware grasps that preserve specific fingers for downstream subtasks. For each task, the robot first selects an appropriate initial grasp of the object (a block), and then executes a second subtask using the remaining fingers. We show real-world rollouts of HANDFUL using a LEAP Hand [28] for tasks that involve pushing (top left), pressin… view at source ↗

**Figure 2.** Figure 2: Overview of HANDFUL. (A, Sec. IV-A): Multiple grasping policies are trained with active and inactive finger constraints to encourage resource-aware grasps that preserve fingers for future manipulation. (B, Sec. IV-B): For each grasp, a second-stage manipulation policy is trained via a multi-stage curriculum, where survivor policies are selected at increasing environment difficulty levels. (C, Sec. IVC): S… view at source ↗

**Figure 3.** Figure 3: Overview of HANDFUL-Bench (Sec. V). The top row shows the starting scenarios for the five tasks. The middle row shows the outcome after grasping the block with resource-aware grasping policies. The bottom row shows successful configurations after performing the second subtask while preserving the initial grasp. The examples above are test-time executions of HANDFUL. • Pick Second: The robot must grasp and … view at source ↗

**Figure 4.** Figure 4: Performance of HANDFUL with curriculum learning vs without curriculum learning over full training with 10 million steps. test for our selection criteria: training is volatile due to tight constraints on in-hand space and finger placement, and this volatility is consequential. Accidentally eliminating the top 2-3 grasp candidates can cause a larger performance drop off compared to tasks such as Push Object,… view at source ↗

**Figure 5.** Figure 5: Left: the real world experiment setup with the xArm7 robot, LEAP hand, and the two Intel L515 cameras indicated with dotted boxes. Right: the different objects we use for experiments. Tasks Push Object Press Button Twist Knob Pull Drawer Pick Second Success (Both) 10 8 6 6 4 Fail Subtask 1 Only 4 4 6 6 7 Fail Subtask 2 Only 0 0 0 0 1 Fail Both 1 3 3 3 3 TABLE IV: Real-world experiment results. For each tas… view at source ↗

**Figure 6.** Figure 6: Hand poses in the human teleoperated data. Across tasks, operators predominantly relied on the index finger for the second subtask and used the remaining fingers for grasp stabilization, reflecting a consistent bias and limited pose diversity. VIII. LIMITATIONS Our experiments use a relatively controlled setting, with state-based observations and limited randomization (e.g., grasping only a block). This si… view at source ↗

**Figure 7.** Figure 7: Success rates of the nine grasping policies in simulation versus real-world settings. Each point represents a policy evaluated on either the black or red block. The dashed line y = x denotes the ideal case with no sim-to-real gap, where success rates in simulation and real world are identical. While in reality, simulation performance remains consistently high, real-world results exhibit substantial variabi… view at source ↗

read the original abstract

Dexterous robot hands offer rich opportunities for multifunctional manipulation, where a robot must execute multiple skills in sequence while maintaining control over previously grasped objects. Most prior work in dexterous manipulation focuses on single-object, single-skill tasks. In contrast, our insight is that many sequential tasks require resource-aware grasps that conserve fingers for future actions. In this paper, we study sequential grasp-conditioned dexterous manipulation, where a robot first grasps an object and then performs a second, distinct manipulation subtask while preserving the initial grasp. We introduce HANDFUL, a learning framework that models finger usage as a limited resource and encourages exploration of resource-aware grasps through finger-level contact rewards. These grasps are subsequently selected for downstream tasks via curriculum-based policy learning. We further propose HANDFUL-Bench, a simulation benchmark that introduces sequential dexterous manipulation tasks across multiple secondsubtask objectives, including pushing, pulling, and pressing, under a shared grasp-conditioned setup. Extensive simulation results demonstrate that prioritizing resource-aware grasps improves second-subtask success and robustness compared to a baseline that greedily optimizes the initial grasp before attempting the second subtask. We additionally validate our approach on a real dexterous LEAP hand. Together, this work establishes resource-aware grasp planning as a key principle for multifunctional dexterous manipulation. Supplementary material is available on our website: https://handful-dex.github.io.

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.

Desk Editor's Note private letter to a colleague

HANDFUL shows that finger-level contact rewards plus curriculum learning can improve second-task success after an initial grasp, but the gains over the greedy baseline need tighter controls to confirm they come from the resource modeling.

read the letter

The paper's main contribution is a framework that treats available fingers as a scarce resource during grasp selection. It adds per-finger contact rewards to push the policy toward grasps that leave some digits free, then uses staged curriculum training so the same policy can handle a second distinct action like pushing or pressing while keeping the first grasp intact. They back this with a new simulation benchmark covering several object and task combinations, plus a hardware check on the LEAP hand that shows the approach transfers without retraining from scratch.

Referee Report

3 major / 2 minor

Summary. The paper introduces HANDFUL, a reinforcement learning framework for sequential grasp-conditioned dexterous manipulation. It models finger usage as a limited resource and employs finger-level contact rewards to encourage grasps that leave fingers available for a subsequent distinct subtask (e.g., pushing, pulling, or pressing an object). The method uses curriculum-based policy learning to select such grasps, is evaluated on the new HANDFUL-Bench simulation benchmark across multiple second-subtask objectives, and is validated on a real LEAP hand. The central empirical claim is that resource-aware grasps improve second-subtask success and robustness relative to a greedy baseline that optimizes only the initial grasp.

Significance. If the results hold under matched conditions, the work would usefully highlight resource awareness as a design principle for multifunctional dexterous manipulation, moving beyond single-skill tasks. The HANDFUL-Bench benchmark and the real-robot demonstration are concrete contributions that could support follow-on research. The approach is entirely empirical (RL with hand-crafted rewards and curriculum), so it does not offer parameter-free derivations or machine-checked proofs, but the reproducible experimental setup on a public benchmark is a positive feature.

major comments (3)

[Results / baseline description] Results section (baseline comparison): The abstract and methods describe the greedy baseline only at a high level as one that 'greedily optimizes the initial grasp.' It is not stated whether this baseline uses the identical policy architecture, action space, curriculum stage thresholds, and exploration noise schedule as HANDFUL. Without this matching, the reported gains in second-subtask success cannot be unambiguously attributed to the finger-level contact rewards rather than differences in the overall learning procedure.
[Methods / reward and curriculum] Methods (reward formulation): The finger contact reward scales and curriculum stage thresholds are listed as free parameters. The central claim that these rewards produce grasps preserving both stability and finger availability rests on the empirical outcomes; however, no ablation is described that varies these scales while holding all other components fixed, leaving open whether the reported robustness is sensitive to hyperparameter choice.
[Experiments / real-robot] Real-robot validation paragraph: The abstract states that the approach is validated on a physical LEAP hand, yet no quantitative details (number of trials, success metric definitions, or sim-to-real transfer protocol) are provided in the summary. This information is load-bearing for the robustness claim that extends beyond simulation.

minor comments (2)

[Abstract] The abstract refers to 'secondsubtask' without a hyphen or space; consistent hyphenation should be used throughout.
[Figures and tables] Figure captions and table headers should explicitly state the number of random seeds and total episodes used for each reported success rate to allow readers to assess statistical reliability.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment point by point below, clarifying our approach and committing to revisions that strengthen the manuscript without altering its core claims.

read point-by-point responses

Referee: [Results / baseline description] Results section (baseline comparison): The abstract and methods describe the greedy baseline only at a high level as one that 'greedily optimizes the initial grasp.' It is not stated whether this baseline uses the identical policy architecture, action space, curriculum stage thresholds, and exploration noise schedule as HANDFUL. Without this matching, the reported gains in second-subtask success cannot be unambiguously attributed to the finger-level contact rewards rather than differences in the overall learning procedure.

Authors: We agree that explicit matching details are necessary to isolate the contribution of the finger-level contact rewards. In the revised manuscript, we will expand the Methods and Results sections to state that the greedy baseline employs exactly the same policy architecture, action space, curriculum stage thresholds, and exploration noise schedule as HANDFUL. The only difference is the absence of the finger contact reward term, so that performance differences can be attributed directly to resource-aware grasp selection. revision: yes
Referee: [Methods / reward and curriculum] Methods (reward formulation): The finger contact reward scales and curriculum stage thresholds are listed as free parameters. The central claim that these rewards produce grasps preserving both stability and finger availability rests on the empirical outcomes; however, no ablation is described that varies these scales while holding all other components fixed, leaving open whether the reported robustness is sensitive to hyperparameter choice.

Authors: We acknowledge that an ablation on the reward scales and curriculum thresholds would strengthen the robustness claim. We will add such an ablation study to the revised manuscript (or supplementary material), systematically varying these hyperparameters while keeping the policy architecture, curriculum structure, and other components fixed. This will show that the reported gains in second-subtask success and robustness hold across reasonable ranges of these parameters. revision: yes
Referee: [Experiments / real-robot] Real-robot validation paragraph: The abstract states that the approach is validated on a physical LEAP hand, yet no quantitative details (number of trials, success metric definitions, or sim-to-real transfer protocol) are provided in the summary. This information is load-bearing for the robustness claim that extends beyond simulation.

Authors: We will revise the real-robot validation section to include the requested quantitative details: the number of trials performed, precise definitions of the success metrics used, and a description of the sim-to-real transfer protocol (including any domain randomization or calibration steps). These additions will make the physical validation reproducible and directly support the robustness claims. revision: yes

Circularity Check

0 steps flagged

No significant circularity: empirical RL framework with independent validation

full rationale

The paper proposes HANDFUL as an RL-based framework using finger-level contact rewards to encourage resource-aware grasps, followed by curriculum policy learning and evaluation on HANDFUL-Bench. The central claims rest on simulation results comparing to a greedy baseline and real-robot validation, with no mathematical derivations, first-principles predictions, or fitted parameters presented as outputs. No self-citations, ansatzes, or renamings reduce the method to its inputs by construction; the approach is a standard empirical pipeline relying on external benchmarks and matched experimental conditions.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

The approach depends on standard robotics simulation assumptions and RL training procedures, along with several tunable reward and curriculum parameters whose specific values are not detailed in the abstract.

free parameters (2)

Finger contact reward scales
Weights balancing grasp stability against resource conservation in the RL objective.
Curriculum stage thresholds
Parameters controlling progression between grasp learning and second-subtask policy training.

axioms (2)

domain assumption Simulation physics sufficiently approximates real dexterous hand dynamics for policy transfer
Required for training in HANDFUL-Bench before real LEAP hand validation.
domain assumption RL can jointly optimize grasp and downstream manipulation under shared finger constraints
Foundational to the curriculum-based policy learning approach.

pith-pipeline@v0.9.0 · 5571 in / 1440 out tokens · 70718 ms · 2026-05-07T16:14:58.056146+00:00 · methodology

discussion (0)

Reference graph

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Predictivity of Early Curriculum Stages:The pat- tern observed forPick Secondin the main paper holds broadly across tasks. The top-performing candidates in the final curriculum stage consistently rank among the leaders in earlier stages, confirming that early performance is a reliable signal for grasp selection. Early curriculum stages seem more predictiv...
[46]

The best-performing candidate grasp is never prematurely eliminated in any seed of any task, and the second and third-best candidates are often retained

Selection Stability Across Seeds:Selection stability is similarly strong across tasks. The best-performing candidate grasp is never prematurely eliminated in any seed of any task, and the second and third-best candidates are often retained. As inPick Second, there is some volatility in selecting these second and third-best grasps, but the impact is minor ...
[47]

In our experi- ments, candidates such asπ 1 andπ 8 perform strongly across nearly all tasks, which may reflect broad task compatibility, high grasp stability in this seed, or both

Limitations:Because we train only a single grasping seed per candidate strategy, it is difficult to cleanly attribute each grasp’s second-subtask performance to its intrinsic task suitability versus the stability of that particular grasp instance (which may vary with the training seed). In our experi- ments, candidates such asπ 1 andπ 8 perform strongly a...