{"id":"ee791657-97b2-47fe-a43a-6f1f6c0c87fd","arxiv_id":"2411.15753","paper_version":2,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":8,"one_line_summary":"FoAR uses a future-contact predictor to gate force/torque features into a vision-based imitation policy and adds a reactive nudge, beating vision-only and naive fusion baselines on three real contact-rich tasks.","lead":"FoAR is a robot control policy that combines camera views with high-frequency force and torque readings to handle contact-rich tasks like wiping, peeling, and chopping. A learned 'future contact' predictor decides when to trust force feedback, and a simple position controller gets a small reactive nudge when contact is missing, achieving strong real-robot results with only tens of demonstrations.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Chopping results omit all baselines except RISE, so 'outperforms all baselines across tasks' is not fully supported.","rationale":"The reader's weakest_assumption focused on the reactive control heuristic (Algorithm 1, Lines 12-14) and its manually chosen thresholds. That is a legitimate generalization concern, but it does not directly threaten the empirical comparisons as reported; even if the heuristic is sensitive to epsilon or thresholds, the reported scores could still stand for the chosen parameters. The missing Chopping baselines, by contrast, directly undermines the abstract's explicit claim that FoAR 'significantly outperforms all baselines across various challenging contact-rich tasks.' A comparative claim requires the comparison set to be present for every task where the claim is made. The Chopping omission is concrete, verifiable from the paper's own tables, and resolvable by a relatively cheap experiment. Because the concern is about missing evidence rather than an internal contradiction, it does not change the verdict from CONDITIONAL; it reinforces that the paper should not be accepted without either adding the missing baselines or amending the claim to say 'outperforms all baselines on the three evaluated tasks with full baseline comparisons' and 'outperforms RISE on Chopping.' I therefore recommend no change to the reader's CONDITIONAL verdict.","tokens_in":903,"tokens_out":853,"duration_ms":69982,"concrete_test":"Run ACT, Diffusion Policy, RISE (force-token), and RISE (force-concat) on the Chopping task under the same protocol used for FoAR and RISE: 10 trials each, same random object placements, same scoring rubric (number of segments, average normalized length, standard deviation of normalized length, ASR). If any baseline attains # Segments >= 3.9, or Avg. Norm. Length <= 0.353, or ASR >= 70, the 'outperforms all baselines' claim fails and the paper's central claim must be qualified. If all baselines fall below FoAR on these metrics, the concern is resolved and the claim is supported.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim, stated in the abstract and echoed in Sections I and IV, is that FoAR 'significantly outperforms all baselines across various challenging contact-rich tasks.' The evidence for Wiping, Wiping (General), and Peeling (Table I) includes all six baselines and ablations, but the Chopping task (Table II) reports only RISE, FoAR, and the demonstration oracle. Section IV-A explicitly states: 'we run 10 trials each only for FoAR and RISE [44] on the Chopping task to conserve resources.' ACT, Diffusion Policy, RISE (force-token), and RISE (force-concat) are not evaluated on Chopping. Thus the universal claim 'all baselines' is not supported for an entire task family. This is not a cosmetic omission: Chopping is the only 'instantaneous force impact' task, and the relative ranking of baselines there could differ from the surface force control tasks. For example, Diffusion Policy might handle short-horizon impacts differently. If any omitted baseline matched or exceeded FoAR's Chopping metrics, the abstract's unqualified claim would be false as stated. The paper acknowledges the resource constraint, so this is a missing-support issue rather than an accusation of selective reporting, but the published claim is broader than the evidence.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"FoAR is a force-aware reactive policy for contact-rich manipulation built on top of the RISE visuomotor policy. It uses a learned future contact predictor to gate the fusion of high-frequency force/torque features with visual scene features, and it adds a reactive control step at deployment: when the predicted contact probability is high but the measured wrench is low, the end-effector action is nudged by a fixed step along the predicted action direction. The paper reports real-robot experiments on Wiping, Wiping (General), Peeling, and Chopping, plus robustness evaluations under dynamic disturbances, claiming that FoAR significantly outperforms all baselines.","tokens_in":13111,"tokens_out":7754,"duration_ms":73455,"significance":"If the empirical claims hold, FoAR would be a practically valuable contribution: it shows that force/torque feedback can be integrated into a position-controlled imitation policy through a learned contact gate plus a simple reactive nudge, and it demonstrates large margins over the RISE baseline on several contact-rich tasks. The paper includes real-robot evaluations, ablations of the predictor and of sensing frequency, and robustness tests, which are useful for the community. However, the strength of the current claims exceeds the evidence: the Chopping task omits most baselines, no error bars or significance tests are reported for the main tables, the predictor labels and reactive thresholds are manually chosen without sensitivity analysis, and one ablation table is inconsistent with the main results. These issues need to be addressed before the paper can be recommended for publication.","major_comments":[{"comment":"The abstract and Section IV claim that FoAR 'significantly outperforms all baselines across various challenging contact-rich tasks,' but the Chopping task evaluates only FoAR and RISE, as stated in §IV-A ('10 trials each only for FoAR and RISE on the Chopping task to conserve resources'). ACT, Diffusion Policy, and the three RISE ablation variants are missing from Table II. The universal 'all baselines' claim is therefore not supported for the instantaneous-force-impact task. Please either evaluate all baselines on Chopping or explicitly qualify the claim to the tasks where all baselines were run.","section":"§IV-C, Table II"},{"comment":"The term 'significantly outperforms' is used throughout, but Tables I and IV report no error bars, confidence intervals, or significance tests, and trial counts are 20 per method (10 for Chopping). With these sample sizes and with hand-assigned scores (e.g., 1/0.5/0 for wiping), the reported margins cannot be assessed for statistical reliability. Please report per-trial results, confidence intervals, and a significance test where appropriate, or replace 'significantly' with a weaker qualitative claim.","section":"Tables I, II, and IV"},{"comment":"The ground-truth labels for the 'future contact predictor' are described in Appendix A as generated from force/torque data 'within the time window [t − 2s, t + 2s]', which is a symmetric window that includes past contact. This makes the predictor a smoothed contact detector rather than a strictly future predictor, and it blurs the claimed distinction from the 'contact detection' variant discussed in §IV-D and Table III. Please specify the exact temporal window and, if future prediction is intended, use labels from a future-only window; otherwise rename the quantity and adjust the interpretation of the ablation.","section":"§III-B Supervision, Appendix A"},{"comment":"The reactive control parameters (δφ = 0.9, δf = 8 N, δt = 5 N·m, ε = 0.006 m) and the predictor supervision threshold are all manually chosen, and no sensitivity analysis is reported. Since the predictor labels and the reactive trigger share the same threshold family, the reported gains could depend on these hand-tuned values rather than on the learned force-awareness. This is a load-bearing generalization concern: please add a sensitivity study over thresholds and ε, or demonstrate that the method works with thresholds set by a principled, task-independent rule.","section":"§IV-A Implementation, Appendix A, Algorithm 1"},{"comment":"The Peeling ablation in Table V reports RISE score 0.293 and FoAR score 0.588, whereas Table I reports RISE 0.377 and FoAR 0.756 for the same task and protocol. The discrepancy is not explained; if these numbers come from different seeds, evaluation splits, or a different version of the method, that must be stated. As printed, the two tables are inconsistent and prevent the reader from trusting the ablation comparison.","section":"Table V vs. Table I"}],"minor_comments":[{"comment":"The checkmarks in Table III are not aligned with the column headers ('w. Predictor', 'w. Reactive'), making it impossible to tell which rows include which components. Please reformat the table so each row clearly indicates the presence or absence of the predictor and reactive control.","section":"Table III"},{"comment":"The sentence 'we run 20 trials per method for the Wiping and Peeling tasks, and 10 trials each only for FoAR and RISE on the Chopping task' is unclear because 'each only for FoAR and RISE' could be read as applying to the whole sentence. Please rephrase to state explicitly which methods are evaluated on which tasks.","section":"§IV-A"},{"comment":"The abstract says 'With only 50 demonstrations per task,' but the Chopping task uses 40 demonstrations. Please adjust the wording to reflect the actual per-task demonstration counts.","section":"Abstract, §IV-A"},{"comment":"In Table IV, the RISE baseline achieves a higher score under the 'Move' condition (0.600) than under 'Original' (0.500), which is counterintuitive for a disturbance evaluation. A sentence explaining this result would help the reader interpret the robustness comparison.","section":"Table IV"},{"comment":"The reactive control correction in Lines 12-14 adds a fixed 6 mm displacement to all predicted position actions without any validation of the step size ε or the averaging horizon Tf = 5. Please add a brief discussion or a small ablation addressing the sensitivity to these values.","section":"§III-C, Algorithm 1"}],"recommendation":"major_revision","confidential_remarks":"The core idea is interesting and the real-robot results are potentially strong, but the manuscript currently overstates its empirical support. The missing Chopping baselines, absent statistical testing, and unaddressed threshold sensitivity are fixable within the scope of a revision; I would not reject the paper outright. I would also like the editor to ensure that the final version reconciles the inconsistent Peeling numbers between Table I and Table V."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"First, what this paper actually gives you: a simple, effective recipe. FoAR takes the RISE point-cloud policy and adds a future contact predictor that learns to gate the fusion of wrist force/torque features into the policy. A separate deployment-time reactive nudge corrects the commanded motion when the predictor says contact is coming but the measured wrench says the push isn't happening. Both pieces are simple, and the real-robot numbers are large: Wiping goes from RISE's 0.500 to 0.875, Peeling from 0.377 to 0.756. The ablations are honest; each design choice (predictor, reactive control, 100 Hz sensing) earns its keep, and the FoAR (3D-cls) failure is a nice sanity check that sharing the vision encoder with the predictor hurts both.\n\nThe main soft spot is reporting discipline, not the method. The abstract says 'significantly outperforms all baselines across various challenging contact-rich tasks,' but the Chopping task—the only instantaneous-impact task—only compares FoAR against RISE. The other baselines and the force-fusion variants are missing there, so 'all baselines' is not supported for that task family. The 'significantly' is also not backed by error bars or tests for Wiping/Peeling; Table I and IV give point estimates from 20 trials. The shared force/torque thresholds between ground-truth contact labels and the reactive trigger do create a real circularity worry, and a sensitivity analysis of δf, δt, and the nudge step ϵ would answer it. No code or data is released, which makes the numbers harder to verify.\n\nNone of this is fatal. The mechanism is clean and the robustness evaluations, where the whiteboard is moved or rewritten mid-task, are a genuine plus. This paper is for people who want to put force feedback on a position-controlled arm without re-engineering the controller. I'd send it to review. The referee should ask for full baseline coverage on Chopping, error bars, and threshold sensitivity, but the core contribution deserves a careful look. I'd probably accept after those revisions.","headline":"A clean, effective recipe for force-aware contact-rich manipulation that deserves review but needs better reporting on the Chopping baselines and error bars.","tokens_in":13602,"tokens_out":2391,"would_cite":true,"duration_ms":21482,"reading_group":"yes","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"FoAR claims that gating force/torque feedback by a learned future-contact probability lets a vision-based imitation policy perform contact-rich manipulation with simple position control, beating vision-only and naive-fusion baselines on…","keywords":["force-aware policy","contact-rich manipulation","future contact predictor","force/torque sensing","multimodal feature fusion","reactive control","imitation learning","robot manipulation"],"falsifier":"Sweep $\\epsilon$ from 0 to 20 mm and $\\delta_f$ from 2 to 20 N on the wiping and peeling tasks, or run FoAR unchanged on objects of different stiffness and curvature using the same thresholds; if performance has no plateau, or the optimal values move with the object, then the reactive control is task-tuned rather than a general contact-rich manipulation mechanism.","tokens_in":12609,"feed_emoji":"🦾","tokens_out":8329,"duration_ms":69545,"temperature":0.7,"pith_summary":"FoAR tries to establish that the missing ingredient for contact-rich manipulation is not a more complex force controller but a way to let force/torque data speak only when contact is actually relevant. The paper combines a point-cloud visuomotor policy with high-frequency force/torque history and a learned future-contact probability that gates the fusion of the two modalities. The same probability also triggers a reactive control step at deployment, so a robot can compensate for insufficient contact force using plain position control. On wiping, wiping with arbitrary board orientation, peeling, and chopping, FoAR reports higher scores than vision-only and naive-fusion baselines, with 100% action success on the first three tasks.","feed_headline":"Future-contact gating lifts wiping, peeling, chopping scores","feed_subtitle":"A learned contact probability routes force feedback only when needed, making simple position control reliable for contact-rich work.","key_machinery":"The load-bearing mechanism is the future contact predictor and the gated fusion it controls. The predictor is a small network that takes the current RGB image and the recent force/torque stream and outputs $\\phi(t)$, trained with binary cross-entropy against contact labels extracted from demonstrations using the same static wrench thresholds used at deployment. Its output multiplies the force feature before concatenation with the scene feature, and at inference it decides whether the current time step belongs to the contact or non-contact temporal ensemble buffer. The reactive control law then closes the loop: if the policy predicts contact but the sensor reads too little force, it nudges the whole predicted action chunk a fixed distance toward its own average direction. This is the mechanism that lets a position-controlled end-effector perform sustained-contact skills.","core_discovery":"The central discovery is that contact-rich manipulation can be treated as a phase-gating problem rather than a force-control problem. FoAR represents the current scene as a sparse point-cloud feature $h^s_t$, encodes the last two seconds of 100 Hz wrench data into a force feature $h^f_t$, and learns a future contact probability $\\phi(t)\\in[0,1]$ from the current RGB image and wrench history. The fused conditioning feature is $h_t = [h^s_t;\\, \\phi(t)h^f_t + (1-\\phi(t))h^*]$, where $h^*$ is a learnable neutral embedding, so wrench information is emphasized exactly when contact is coming and blended out during free motion. At deployment, when $\\phi$ exceeds 0.9 but the measured wrench is below 8 N force and 5 N·m torque, the controller shifts the predicted end-effector trajectory by $\\epsilon=0.006$ m along the average predicted motion direction. The paper reports scores of 0.875, 0.850, and 0.756 on Wiping, Wiping(General), and Peeling versus 0.500, 0.500, and 0.377 for the strongest vision-only baseline, and chopping results of 3.9 versus 1.8 segments with lower length dispersion.","pith_inferences":["The hand-set thresholds ($\\delta_\\phi=0.9$, 8 N, 5 N·m, $\\epsilon=0.006$ m) are not learned, and the paper itself notes static thresholds may struggle in complex environments; a natural extension is to make the nudge size or wrench threshold depend on predicted contact state or object stiffness.","The same gated-fusion idea should transfer to other noisy-but-informative modalities such as tactile or audio, where the signal-to-noise ratio also switches with contact phase; the paper only demonstrates it for force/torque.","A testable extension is to measure how much of the gain comes from the 100 Hz sampling rate by retraining FoAR with downsampled wrench histories; the paper's ablation table suggests the advantage degrades sharply at lower rates.","Because the reactive correction only uses the average predicted direction, it should also work when the action predictor is replaced by a different one, as long as a contact probability and wrench readings are available; FoAR's own experiments do not test this portability."],"forward_implications":["If FoAR's results hold, contact-rich imitation policies do not need compliant or hybrid force controllers; a reactive position-level nudge driven by a contact predictor is enough for the evaluated skills.","Gating by predicted future contact should remove the need to choose a single fusion strategy for an entire task, since force information is suppressed during phases where it is mostly noise.","The same predictor can serve two roles — feature weighting and control triggering — so the policy and the reactive controller are co-designed rather than tuned separately.","With 50 demonstrations per task, the method reaches 100% grasp and contact-action success on wiping and peeling, suggesting the force gate adds capability without requiring more data.","Because the system kept its score when the whiteboard was rewritten or moved mid-task, the reactive mechanism appears to absorb unexpected state changes rather than needing replanning."],"supporting_citations":[{"why":"The 3D point-cloud imitation policy FoAR builds on; provides the scene encoder, transformer backbone, and the primary vision-only baseline scores.","marker":"[44]"},{"why":"Supplies the diffusion-based action denoising head FoAR uses, and a strong end-to-end vision baseline for comparison.","marker":"[10]"},{"why":"Provides the action-chunking transformer baseline and the temporal ensemble buffer mechanism FoAR adapts for contact versus non-contact phases.","marker":"[52]"},{"why":"A force-centric imitation learning method that motivates including wrench information in contact-rich policies and serves as comparison for force-domain approaches.","marker":"[31]"},{"why":"An adaptive compliance policy that integrates force/torque with vision; represents prior work whose contact-phase-only assumption FoAR challenges.","marker":"[21]"},{"why":"A force-domain diffusion policy that feeds desired forces to impedance control, illustrating an alternative to FoAR's position-control reaction.","marker":"[46]"},{"why":"A visuo-tactile transformer used to justify the force-token fusion variant that FoAR's gated fusion is designed to improve upon.","marker":"[9]"},{"why":"A transformer-based generalist policy whose token-fusion style informs the force-token ablation and the claim that naive token fusion lets noisy force data hurt non-contact phases.","marker":"[37]"}],"fun_headline_variants":["FoAR gates force feedback by predicted contact, boosting contact-rich tasks","Phase-gated force sensing lifts wiping, peeling, chopping performance","Future-contact probability routes wrench data only when needed, beating baselines","Simple position control becomes reliable via reactive force-aware gating"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The load-bearing premise is that a fixed manual rule — if predicted contact exceeds 0.9 but measured wrench stays below 8 N and 5 N·m, nudge the end-effector 6 mm along the predicted motion direction — is the right corrective behavior across objects and contact geometries, rather than a heuristic calibrated to the three tested tasks.","fun_headline_variants_meta":{"raw":{"variants":["FoAR gates force feedback by predicted contact, boosting contact-rich tasks","Phase-gated force sensing lifts wiping, peeling, chopping performance","Future-contact probability routes wrench data only when needed, beating baselines","Simple position control becomes reliable via reactive force-aware gating"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000684,"raw_usage":{"total_tokens":3122,"prompt_tokens":981,"completion_tokens":2141,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":597,"completion_tokens_details":{"reasoning_tokens":2068}},"tokens_in":597,"tokens_out":2141,"duration_ms":16285,"temperature":1.0,"reasoning_tokens":2068,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-12T13:56:04.210489+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Sweep $\\epsilon$ from 0 to 20 mm and $\\delta_f$ from 2 to 20 N on the wiping and peeling tasks, or run FoAR unchanged on objects of different stiffness and curvature using the same thresholds; if performance has no plateau, or the optimal values move with the object, then the reactive control is task-tuned rather than a general contact-rich manipulation mechanism.","supporting_citations":[{"cited_title":"Diffusion Policy: Visuomotor Policy Learning via Action Diffusion","cited_arxiv_id":null,"evidence_quote":"Supplies the diffusion-based action denoising head FoAR uses, and a strong end-to-end vision baseline for comparison."},{"cited_title":"Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware","cited_arxiv_id":null,"evidence_quote":"Provides the action-chunking transformer baseline and the temporal ensemble buffer mechanism FoAR adapts for contact versus non-contact phases."},{"cited_title":"Visuo-Tactile Transformers for Manipulation","cited_arxiv_id":null,"evidence_quote":"A visuo-tactile transformer used to justify the force-token fusion variant that FoAR's gated fusion is designed to improve upon."},{"cited_title":"Octo: An Open-Source Generalist Robot Policy","cited_arxiv_id":null,"evidence_quote":"A transformer-based generalist policy whose token-fusion style informs the force-token ablation and the claim that naive token fusion lets noisy force data hurt non-contact phases."}],"review_version":1}