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ForceBench: Benchmarking Force Integration for Robotic Manipulation

Xuan Han, Zinuo Jin, Tianlin Li, Yunfan Lou, Xin Liu, Yuchen Xue, Yuanpeng He, Boxian Yao, Shuhan Zhan, Ruihai Wu, Zhibo Pang

arXiv:2610.19604v1cs.RO

TL;DR

Existing force-aware systems differ in tasks, demonstrations, and control settings, making direct comparisons difficult. ForceBench supplies a unified benchmark and compares force-integration designs across manipulation demands. Prompt Fusion leads the reported outcomes, while results show task-dependent benefits and limitations.

  • Problem

    Force-aware systems vary in tasks, demonstrations, and control settings, and aggregate success rates can obscure differences across manipulation demands.

    Source: §I · p. 2

  • Method

    ForceBench combines eight tasks and 1,600 synchronized demonstrations under a unified protocol, then compares force-integration designs on a shared VLA backbone.

    Source: §I · p. 2

  • Results

    Prompt Fusion improves every real-world task and ties Dual-Path Fusion at 63.00% mean success, versus 40.00% for the baseline; temporal gains concentrate on physical-property inference, while temporal designs underperform on assembly.

    Source: §I · p. 2, §VI · p. 8

  • Takeaways & Limitations

    ForceBench supports identifying task-dependent benefits and limitations of force integration across manipulation demands.

    Source: §VI · p. 8

Abstract

from arXiv · show

Force feedback provides vision-language-action (VLA) policies with physical observations that can guide action selection and execution. However, differences in tasks, demonstrations, and control settings across existing systems hinder reliable comparisons of force integration designs. We introduce ForceBench, a simulation benchmark for evaluating six-axis wrist-force integration under a unified protocol. ForceBench combines eight tasks spanning physical-property inference, resistance-dependent interaction, and contact-rich assembly with 1,600 synchronized demonstrations and task-specific success criteria. We compare one no-force baseline and six force integration designs on a shared $\pi_{0.5}$ backbone. Prompt Fusion achieves the highest observed mean simulation success of 35.31%, compared with 23.25% for the baseline, and improves success rates across all three task categories. Predictive and temporal gains in simulation concentrate on physical-property inference, while both temporal designs underperform the baseline on assembly. Real-world evaluation of eight systems across five tasks supports the main qualitative trends: Prompt Fusion and Dual-Path Fusion achieve 63.00% mean success, compared with 40.00% for the baseline, but only Prompt Fusion improves every task. These findings support ForceBench's practical value for identifying the benefits and limitations of force integration across manipulation demands. The project page is available at https://forcebench-platform.github.io/.

I. INTRODUCTION

ForceBench addresses the difficulty of comparing force-integration designs across mismatched tasks, demonstrations, and control settings with a unified benchmark and comparative study. Prompt Fusion leads the reported results, while real-world findings show that similar aggregate success can conceal task-dependent tradeoffs.

I. INTRODUCTION

  • Existing force-aware systems differ in tasks, demonstrations, and control settings, while aggregate success can obscure differences across manipulation demands.The paper asks how to compare force-integration designs reliably under a unified protocol.Source: §I · p. 2
  • Prompt Fusion reaches 35.31% mean simulation success versus 23.25% for the no-force baseline; in real-world trials, it ties Dual-Path Fusion at 63.00% and alone improves every task.In simulation, predictive and temporal gains concentrate on physical-property inference, while both temporal designs underperform the baseline on assembly.Source: §I · p. 2
  • ForceBench unifies eight tasks, 1,600 synchronized demonstrations, and task- and category-level evaluation for six-axis wrist-force integration.Its tasks span physical-property inference, resistance-dependent interaction, and contact-rich assembly.Source: §I · p. 2
  • The study compares observation conditioning, predictive supervision, and temporal force representations on a shared VLA backbone across three manipulation demands.Real-world evaluation of eight systems across five tasks supports the main qualitative simulation trends and reveals task-dependent tradeoffs.Source: §I · p. 2

II. RELATED WORK

  • Prior work explores force-token fusion, temporal encoding, contact prediction, action–wrench modeling, and force-to-vision distillation; ForceBench complements existing vision benchmarks with shared demonstrations and a unified force-integration protocol.Existing benchmark suites cover vision-based manipulation and other settings, but the cited passage identifies no standardized comparison protocol for six-axis wrist-force integration.Source: §II · pp. 2–3

III. FORCEBENCH

ForceBench evaluates six-axis wrist-force integration across eight tasks using parameterized conditions, expert demonstrations, and seeded evaluation. The tasks cover hidden physical properties, resistance-dependent interactions, and geometrically constrained assembly.

III. FORCEBENCH

  • ForceBench combines eight tasks, parameterized conditions, synchronized expert demonstrations, seeded evaluation, and a shared observation–action interface across three manipulation categories.Task-specific decision and completion metrics distinguish physical decisions from subsequent execution.Source: §III · p. 3, B. Task Design · p. 3

A. Assets and Physical Mechanisms

  • The benchmark uses a Franka arm with a parallel gripper, with task parameters controlling mass, center of mass, resistance, and initial poses.Resistance mechanisms couple force and moment profiles to articulation state, including direction-dependent resistance, rotational transitions, and axial engagement before turning.Source: A. Assets and Physical Mechanisms · p. 3

B. Task Design

  • Weight Sort and CoM Sort require distinguishing visually identical parcels by mass or rotational load, then placing them according to the inferred property.Success includes correct placement, release, and settling; CoM Sort parcels share appearance and total mass but differ in center of mass.Source: B. Task Design · p. 3
  • Resistance tasks require adapting to changing loads, including crossing valve or stove transitions, coordinating a spring-loaded press, or stopping at a specified drawer resistance.Drawer Pull fails if the drawer is pulled beyond the mechanism’s limit.Source: B. Task Design · p. 3
  • Nut Thread tests constrained assembly by requiring alignment, thread engagement, rotation to a target height, release, and gripper withdrawal.Success also requires the nut to remain centered and upright, with the evaluator checking assembly geometry and completion conditions.Source: B. Task Design · pp. 3–4

C. Expert Demonstrations and Dataset

Task-specific scripted experts generate successful demonstrations, while policy inputs exclude privileged simulator state. Observations and actions are recorded synchronously at the control rate.

C. Expert Demonstrations and Dataset

  • Scripted experts use privileged simulator state and a shared controller to collect 1,600 successful demonstrations, retaining only trajectories that meet task success criteria.The dataset contains 200 demonstrations per task, and privileged state is excluded from policy inputs.Source: C. Expert Demonstrations and Dataset · p. 4
  • Physics runs at 60 Hz, while control and synchronized observation–action recording run at 15 Hz.Source: C. Expert Demonstrations and Dataset · p. 4
  • Policy inputs include external and wrist RGB images, robot state, task language, and a six-axis wrist force observation; recorded actions specify absolute TCP poses and gripper targets.The force observation is the wrist–hand joint reaction expressed in the joint frame without gravity compensation.Source: C. Expert Demonstrations and Dataset · p. 4

D. Parallel Evaluation and Outcomes

The evaluation uses shared-seed layouts, a common controller, and complete task-specific success criteria. The method study separates force-conditioning interfaces from auxiliary future-force supervision, with temporal systems treated as separate references.

D. Parallel Evaluation and Outcomes

  • Policies run on layouts initialized with shared seeds, and episode success requires the complete task-specific criteria, including applicable release, clearance, stability, or completion events.Per-episode records also retain destination choices, mechanism progress, and assembly geometry for diagnosis.Source: D. Parallel Evaluation and Outcomes · p. 4

IV. FORCE INTEGRATION METHODS

  • The method study separates how force conditions action generation from whether future-force prediction provides useful supervision.The predictive comparison retains the Token Fusion input pathway, while temporal systems are separate references rather than a controlled history-length ablation.Source: §IV · p. 4

A. Force as a Policy Observation

  • Token Fusion inserts continuous force tokens before action tokens, Prompt Fusion discretizes normalized force in the prompt, and Cross-Attention conditions the action representation near its output.Source: A. Force as a Policy Observation · p. 5

B. Force as Predictive Supervision

  • Token Fusion + Pred adds ten-step future-force prediction during training, but its prediction head does not select or modify deployment actions.Its objective adds a masked future-force error to the backbone’s action flow-matching loss, using K = 10 future steps and auxiliary weight λ = 3 × 10^-4.Source: B. Force as Predictive Supervision · p. 5

C. Temporal Reference Systems

The temporal reference systems extend force input beyond a single sample while retaining the shared experimental backbone and evaluation protocol. Their designs differ in how temporal statistics reach the policy.

C. Temporal Reference Systems

  • Both temporal systems use separately trained policies to extend a current force sample with temporal statistics.Temporal Prompt leaves the backbone and action expert unchanged; Dual-Path Fusion adds its second pathway, while both use the shared experimental evaluation setup.Source: C. Temporal Reference Systems · p. 5
  • Temporal Prompt appends the current force sample and per-axis mean and standard deviation from the last 16 samples to the robot state, expanding it from 7 to 25 numbers without adding parameters.The 16-sample window spans about one second at 15 Hz; the state is normalized and discretized into numerical prompt tokens as in Prompt Fusion.Source: C. Temporal Reference Systems · p. 5
  • Dual-Path Fusion retains the Temporal Prompt pathway and adds four continuous tokens derived from 25 masked statistics of the same 16-sample window.A two-layer MLP maps the statistics to tokens placed before the action tokens; the pathway window is zeroed for 30% of training samples and always provided at deployment.Source: C. Temporal Reference Systems · p. 5

A. Experimental Design

  • All seven π0.5 configurations share the backbone and demonstrations, train for 10,000 steps, and use shared task seeds; evaluation covers 200 episodes per task across eight tasks.The simulation protocol uses 15 Hz control and 50-step action chunks for the seven π0.5 configurations, executing each chunk before querying again unless an episode ends sooner.Source: A. Experimental Design · p. 5

B. Overall Performance

Prompt Fusion has the highest observed mean simulation success, while results across categories and task diagnostics show that gains vary by manipulation demand and by whether success requires completing execution.

B. Overall Performance

  • Dual-Path Fusion leads physical-property inference at 85.75%, while Prompt Fusion leads resistance-dependent interaction at 20.80%.On Nut Thread, Prompt Fusion ranks highest among the seven π0.5 configurations at 34.00%, while the π0 reference reaches 36.00%.Source: B. Overall Performance · p. 5
  • Future-force supervision raises Token Fusion’s overall mean from 23.56% to 26.25%, with Weight Sort gains but decreased CoM Sort success.The reported task-specific pattern indicates the predictive-supervision benefit is not uniform across physical-property inference tasks.Source: B. Overall Performance · p. 5
  • 35.31% mean simulation success for Prompt Fusion exceeds the no-force baseline by 12.06 percentage points; Dual-Path Fusion reaches 35.13%.Table I reports task success rates over 200 episodes per task–method pair, with the mean equally averaging eight tasks.Source: B. Overall Performance · p. 5, Table I · p. 6

C. Analysis and Discussion

  • Prompt Fusion’s gains on physical-property and resistance-dependent tasks are consistent with jointly designing force encoding and the backbone’s conditioning pathway.Prompt Fusion appends force to proprioceptive state and reuses π0.5’s numerical-conditioning pathway; mass and center of mass persist within an episode while instantaneous wrist readings vary with motion and contact.Source: C. Analysis and Discussion · p. 6

D. What the Failure Structure Reveals

  • For Prompt Fusion, wrong Weight Sort destinations fall from 86 to 17 episodes while incomplete placements remain nearly unchanged; on Faucet Turn, target-angle and crossing conditions rise from 26 to 45 episodes as complete successes rise from 19 to 42.The authors associate these gains with more appropriate destination and stopping outcomes under hidden physical properties and resistance transitions.Source: D. What the Failure Structure Reveals · p. 7
  • Predictive supervision reduces incomplete Weight Sort placements from 84 to 14 and raises successes from 101 to 156, while wrong destinations rise from 15 to 30.Because more episodes reach a destination, the wrong-destination counts alone do not establish lower decision accuracy.Source: D. What the Failure Structure Reveals · p. 7
  • Temporal Prompt meets Faucet Turn angle-and-crossing conditions more often than Prompt Fusion, 98 versus 45 episodes, yet succeeds less often, 35 versus 42.On Drawer Pull, Dual-Path Fusion and Prompt Fusion both meet opening-range and intact-latch conditions in 81 episodes, but achieve 9 and 46 successes, respectively; selected object-state conditions can therefore coexist with lower full-task success.Source: D. What the Failure Structure Reveals · p. 7

E. Alignment with Real-World Manipulation

Real-world evaluation supports the simulation trends, while showing task-dependent tradeoffs among force-integration designs. The conclusion highlights Prompt Fusion’s consistent improvements and ForceBench’s value for comparing those tradeoffs.

E. Alignment with Real-World Manipulation

  • Real-world tests cover eight systems and five tasks across the same three manipulation categories, using 800 trials on shared initial conditions.The setup uses a Franka Research 3 with a six-axis force/torque sensor; each system receives 200 demonstrations per task.Source: E. Alignment with Real-World… · pp. 7–8
  • 63.00% mean success for Prompt Fusion and Dual-Path Fusion, compared with 40.00% for the no-force baseline; only Prompt Fusion improves all five tasks.Both temporal designs exceed Prompt Fusion on Weight Sort but underperform the baseline on Block Insert.Source: E. Alignment with Real-World… · p. 8

VI. CONCLUSION

  • ForceBench combines eight tasks and 1,600 synchronized demonstrations for unified evaluation of physical-property inference, resistance-dependent interaction, and contact-rich assembly.Prompt Fusion has the highest observed mean simulation success and the most consistent task-wise improvements; predictive and temporal gains concentrate on physical-property tasks, while both temporal designs underperform the baseline on assembly.Source: §VI · p. 8

APPENDIX A SIMULATION PROTOCOL AND TASK CRITERIA

The simulation protocol uses shared environment seeds and task-specific expert demonstrations, with common observations, actions, and execution settings. Success criteria combine task-specific physical outcomes with release, stability, and clearance conditions where applicable.

APPENDIX A SIMULATION PROTOCOL AND TASK CRITERIA

  • Evaluation uses shared environment seeds 300–499, with 200 episodes per task and method, and the same 200 successful expert demonstrations per task across methods.Training instances use seeds 0–199; these are environment seeds, not independent training runs.Source: Appendix A · p. 10

A. Observation, Action, and Execution

  • Policies observe external and wrist RGB views, seven robot-state values, and six wrist-force components; actions specify absolute TCP poses and gripper targets.A damped-least-squares inverse-kinematics controller executes targets using synchronized quintic interpolation; observations and action execution advance at 15 Hz, with a 1,500-step episode budget.Source: A. Observation, Action, and Execution · p. 10

B. Physical Parameters and Completion Conditions

  • For Weight Sort and CoM Sort, success requires correct destination selection plus placement, release, and settling; choosing the destination alone is insufficient.Weight Sort varies mass while appearance stays fixed, whereas CoM Sort varies center of mass while appearance and total mass stay fixed.Source: B. Physical Parameters and Completion… · p. 10
  • Faucet Turn and Direction require crossing the resistance peak, water visibility, release, clearance, and stationary conditions; Direction additionally scores the final angle in the seeded easier direction.Stove Turn requires ignition crossing, final-angle tolerance, visible flame, release, and clearance, whereas Stove Press uses a latched event without final-angle, release, or flame-visibility requirements.Source: B. Physical Parameters and Completion… · p. 10
  • Drawer Pull requires stable completion within an accepted opening range, an intact latch, and release; Nut Thread uses geometric alignment, height, tilt, release, and fingertip-clearance checks.For Drawer Pull, merely crossing the opening range does not count; for Nut Thread, the geometric checks are not a separate classifier of thread engagement.Source: B. Physical Parameters and Completion… · p. 10

APPENDIX B FORCE-INTERFACE IMPLEMENTATION DETAILS

The force-interface comparison varies feature placement, normalization, and model capacity, so it does not isolate insertion location alone. Numerical prompt features append current or temporal force statistics to robot state, normalize and discretize them, then pad to the model’s state width.

APPENDIX B FORCE-INTERFACE IMPLEMENTATION DETAILS

  • Input placement, normalization, and model capacity vary across force-interface designs, so the comparison does not change only a single insertion location.Prompt Fusion uses current wrench values, while Temporal Prompt adds per-axis mean and standard deviation over a 16-sample window; both are padded to 32 values before tokenization.Source: Appendix B · p. 11, A. Numerical Prompt Features · p. 11

A. Numerical Prompt Features

  • Numerical prompt features use stored lower and upper quantiles for component-wise normalization, then map values to indices at 256 equally spaced thresholds without explicitly clipping tokenizer inputs to [-1, 1].Temporal Prompt’s window statistics use all supplied entries rather than the validity mask used by the separate Dual-Path token encoder.Source: A. Numerical Prompt Features · p. 11

B. Continuous Force Tokens and Attention

The force-token designs encode current wrench observations for action generation through token insertion or cross-attention. Dual-Path Fusion adds masked temporal statistics, while the predictive variant uses force-token and action-chunk features to supervise future-force prediction.

B. Continuous Force Tokens and Attention

  • Current-wrench encoding maps six force components into six force tokens, which are inserted before action tokens; the action block can attend to force tokens, but the prefix cannot attend to the suffix.Cross-Attention uses the same current-wrench token construction but conditions the action representation after the action expert and before action projection.Source: B. Continuous Force Tokens and Attention · p. 11

C. Dual-Path Statistics and Dropout

  • Dual-Path Fusion summarizes a 16-sample force window with masked statistics, including separate means for the first and second halves, then projects the resulting features into four hidden vectors.Training-time whole-window dropout zeros this pathway’s history and validity mask with probability 0.3 while retaining the numerical prompt; deployment supplies the history without this dropout.Source: C. Dual-Path Statistics and Dropout · p. 11

D. Auxiliary Future-Force Prediction

  • The predictive head combines pooled contextualized force tokens with pooled clean-action estimates to produce ten six-axis future-force predictions.Its weighted loss excludes padded future targets, and the head provides training supervision rather than being evaluated during deployment action sampling.Source: D. Auxiliary Future-Force Prediction · p. 11

APPENDIX C AGGREGATION AND INTERPRETATION OF RESULTS

The section explains how simulation and real-world success rates are aggregated and interpreted across tasks. In the real-world study, Prompt Fusion and Dual-Path Fusion tie overall, but their task-level profiles differ.

A. Episode-Level Success and Category Means

  • Simulation task success uses complete binary outcomes across 200 episodes per task, and equal task sample sizes make the overall rate the percentage of successes among 1,600 episodes.The ten methods in Table I account for 16,000 evaluations.Source: A. Episode-Level Success and Category… · p. 11
  • The simulation categories contain two physical-property tasks, five resistance tasks, and one assembly task; the overall mean weights them by 2/8, 5/8, and 1/8.Table III reports category means and total successes out of 1,600 episodes.Source: A. Episode-Level Success and Category… · pp. 11–12, Table III · p. 12
  • Three episodes separate Prompt Fusion’s 565 simulation successes from Dual-Path Fusion’s 562, a 0.1875-percentage-point gap that is not an established significant advantage.Each simulation task has 200 episodes, and an additional success changes the overall rate by 0.0625 percentage points.Source: A. Episode-Level Success and Category… · pp. 11–12

B. Real-World Study

  • The real-world study uses a Franka Research 3 arm and evaluates eight systems on five tasks from the same 20 initial conditions per task, totaling 800 trials.The tasks span visually similar can sorting, resistance-dependent interactions, and geometrically constrained block insertion.Source: B. Real-World Study · p. 12
  • Prompt Fusion and Dual-Path Fusion each achieve 63 successes out of 100, versus 40 for No Force; only Prompt Fusion improves all five tasks.Dual-Path Fusion improves Weight Sort from 6 to 19 successes but reduces Block Insert from 10 to 4, each out of 20.Source: B. Real-World Study · p. 12

C. Diagnostic and Statistical Scope

The section distinguishes intermediate diagnostics from complete-task success and limits interpretation of aggregate comparisons. Its example wrench traces are illustrative recordings, not policy evaluations, and the simulation and hardware measurements have different acquisition conventions.

C. Diagnostic and Statistical Scope

  • Intermediate-state diagnostics do not replace complete-task success: Prompt Fusion and Dual-Path Fusion both reach Drawer Pull’s opening range in 81 episodes, but complete successes are 46 and 9.Sorting choices and assembly target geometry should also be separated from final release and withdrawal checks.Source: C. Diagnostic and Statistical Scope · p. 12
  • Aggregate success tables do not identify which shared-seed paired episodes differ or quantify variability across independently trained policies, so reported values remain observed performance under the stated protocol.The controlled comparison is limited to one VLA backbone, eight simulation tasks, and one simulated robot embodiment; assembly is represented by one task.Source: C. Diagnostic and Statistical Scope · p. 12

APPENDIX D EXAMPLE SIX-AXIS WRENCH TRAJECTORIES

  • Simulation traces use raw 15 Hz wrist–hand joint reaction wrenches, while hardware traces use sensor-frame readings without recorded coordinate transformation or bias compensation.Their reference conventions differ, so absolute magnitudes are not calibrated for simulation-to-reality comparison.Source: Appendix D · p. 12
  • Figures 7 and 8 show example wrench recordings rather than policy-evaluation rollouts, with force and torque components plotted separately and every recorded sample retained.The simulation examples are successful scripted demonstrations; hardware examples cover all five tasks.Source: Appendix D · p. 12, Figure 7 · p. 13, Figure 8 · p. 14

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