{"id":"30641756-8264-47d4-91f9-37af94835ae6","arxiv_id":"2506.03270","paper_version":3,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":5.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":2,"one_line_summary":"A vision-language interpreter that writes PDDL problems, grounded in task-and-motion planning with failure-feedback revision, outperforms a VLM-as-a-planner baseline on bimanual cooking tasks.","lead":"ViLaIn-TAMP is a hybrid planning system that turns language instructions and camera images into structured robot task plans for two arms, then checks whether those plans can physically move without collisions before execution. On five bimanual cooking tasks, it beat a direct vision-language-model planner in simulated success rate, and its corrective replanning loop improved results further.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Headline performance claim is not stable: the paper reports the baseline margin as 17.5%, 18%, and 10% with no trial counts or confidence intervals, so the central superiority claim is currently unverified.","rationale":"I read the paper as a system integration paper: ViLaIn is a reasonable way to convert multimodal inputs to PDDL, Fast Downward plus modified MTC is a reasonable TAMP backend, and the three-level failure feedback is a genuine contribution. The central claim is not architectural novelty but the reported success-rate advantage. That advantage is the weakest link because the paper's own numbers disagree and the statistical basis is absent. The reader's skill-reliability assumption is a real secondary concern for real-robot claims, but the simulation-based headline can be checked directly from the released logs; I therefore keep the same CONDITIONAL verdict. The authors should supply exact trial counts, error bars, and a reconciliation of the three effect sizes; until then the quantitative advantage over the baseline is not robustly established.","tokens_in":10889,"tokens_out":5082,"duration_ms":56476,"concrete_test":"From the released dataset and code (or by rerunning the five task sets with all listed configurations and a fixed seed), extract per-trial outcomes: for each task, list N (e.g., 10 problems in Pick and Place/Slice Food/Slice and Serve, 9 in Pick Obstacles) and success/failure per configuration, then compute mean success rates with exact 95% Wilson intervals and reconcile the 17.5%/18%/10% and 32.9%/32% margins. If any interval contains zero for the main ViLaIn-TAMP-CP vs Baseline-CP comparison, the abstract's superiority claim should be explicitly softened.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim is a quantitative superiority claim: grounding a VLM in PDDL plus TAMP improves success over direct VLM plan generation. The evidence for that claim is not currently reproducible from the manuscript. The abstract reports a 17.5% mean success-rate margin over the baseline and a 32.9% CP boost; the introduction reports 18% and 32%; Section V-B reports an average margin of 10%. No per-task trial counts, confidence intervals, or significance tests accompany Fig. 5 or Table I, and the aggregate numbers for no-ICL (45%) and with-ICL (55%) are given without raw counts. Since the headline is an effect size, a margin that changes by 7.5 points depending on where in the paper it is stated, and that could be within a few trials of noise for 9–10 problems per task, cannot support the abstract's claim as written. This is not a criticism of the architecture; it is a request for the exact experimental record needed to verify the difference.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes ViLaIn-TAMP, a hybrid planning framework that uses a vision-language interpreter to convert natural-language instructions and scene images into PDDL problem specifications, then grounds these specifications in an integrated Task and Motion Planning system built on Fast Downward and MoveIt Task Constructor for dual-arm cooking tasks. A corrective-planning module feeds structured motion-failure feedback back to the VLM to revise the PDDL problem and replan. The framework is evaluated on five simulated bimanual manipulation tasks and validated on a physical dual-arm UR5e system, with the paper reporting that ViLaIn-TAMP outperforms a direct VLM-as-a-planner baseline and that corrective planning substantially improves success rate.","tokens_in":11027,"tokens_out":6337,"duration_ms":76965,"significance":"The architectural direction is valuable: grounding a VLM in a symbolic planner and a verified motion-planning layer, with iterative correction from structured failure feedback, is a credible way to make VLM-generated plans verifiable and executable for bimanual tasks. The paper also contributes an open dataset, a modified MTC that emits structured failure messages, and a real-robot demonstration, and it evaluates the framework with both proprietary and open-source foundation models. However, the central quantitative claim is not currently supported as reported: the headline success-rate margin changes between the abstract, introduction, and results section, trial counts and confidence intervals are absent for the main comparison, and one load-bearing assumption about the learned slicing skill is unmeasured. These issues are fixable, but they must be addressed before the claimed effect size can be accepted.","major_comments":[{"comment":"The headline success-rate advantage is reported inconsistently: the abstract states 17.5% (with a 32.9% CP boost), the introduction states 18% (with a 32% boost), and §V-B states an average margin of 10% over the baseline. No confidence intervals, significance tests, or per-task trial counts accompany Fig. 5 or Table I for the main GPT-4o evaluation; only the open-source-model ablation in §V-C specifies 50 trials per task. A 7.5-point discrepancy in the central effect size, with no measure of sampling variability, means the claimed superiority over the baseline is not verifiable from the manuscript. Please report exact trial counts, repeated-seed information, confidence intervals, and reconcile the three stated margins.","section":"Abstract, §I, §V-B"},{"comment":"The baseline is described as taking O, L, D as input and directly generating action sequences, while ViLaIn-TAMP takes the raw scene observation S (image) in addition to domain knowledge D and the linguistic instruction L. If O contains only detected object labels, as suggested by the Object Estimator description in §IV-A, then the comparison gives the proposed method access to strictly more perceptual information than the baseline, and part of the reported margin could be attributable to that input asymmetry rather than to grounding in TAMP. Please specify exactly what O contains and either give the baseline the same visual input as ViLaIn-TAMP or explicitly control for the input modality.","section":"§V-A, Baseline-CP"},{"comment":"The integrated TAMP system treats the learned slicing skill as a mock MTC stage and states: \"During execution, we assume the skill reliably returns the robot to the predefined ending pose.\" The real-robot validation in §V-D reports no quantitative measure of skill-level reliability or success, and the text says execution halts if the ending pose is not reached. Because slicing is one of the core long-horizon behaviors evaluated in the paper, the end-to-end success claim is conditional on an unmeasured component; please report skill-level success rates and failure counts from the real-robot experiments.","section":"§IV-B, §V-D"},{"comment":"The numbers for the no-CP condition are difficult to reconcile. Section V-C reports a 45% average success rate for ViLaIn-TAMP-No-CP, but Table I's without-ICL column has per-task success rates of 92, 84, 40, 90, and 80, which average 77.2%. If the 45% refers to a different configuration, such as the baseline or an ablation with different inputs, that must be stated explicitly; as written, a reader cannot tell which condition Table I describes or how the CP boost of 32.9%/32% was computed.","section":"§V-C, Table I"}],"minor_comments":[{"comment":"The notation for the maximum number of corrective-planning attempts alternates among N_CP,max, N CP,max, and N CP, max; please use a single consistent notation throughout.","section":"Algorithm 1, §V-A"},{"comment":"Figure 5 and Table I do not clearly indicate which bars or rows correspond to the with-ICL and without-ICL conditions; the captions should state the color/pattern legend and the exact configuration family (ViLaIn-TAMP-CP vs. ViLaIn-TAMP-No-CP).","section":"Fig. 5, Table I"},{"comment":"Algorithm 1 calls EXECUTE-REAL-ROBOT after every successful plan, but the primary results in §V-B are reported as a mixture of planning and execution success in simulation; please clarify which evaluation results involve real-robot execution and which are simulated.","section":"Algorithm 1, §V-B"},{"comment":"In the Adaptive Execution paragraph, the list items are labeled (A) and (B), but the sentence \"grouping only (A) actions for potential replanning\" is immediately followed by a description of processing (B) actions; rephrasing would make the grouping and replanning logic clearer.","section":"§V-D"}],"recommendation":"major_revision","confidential_remarks":"The paper is within the scope of a robotics journal and the architectural contribution is credible, but the experimental section needs a full statistical and comparison rewrite before I can recommend acceptance. I see no citation or novelty issue; the problem is that the headline effect size is not reproducible from the manuscript as currently written."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"The thing to know: this is a solid integration paper, not a breakthrough, and its main empirical claim is not yet established. The authors combine ViLaIn (their own VLM-to-PDDL interpreter) with MoveIt Task Constructor, add a corrective-planning loop that converts MTC's motion-failure comments into structured text feedback for the VLM, and evaluate on five bimanual cooking tasks. The custom MTC feedback and the dual-arm domain are genuinely new relative to the cited prior work; the bimanual dataset with PDDL problems, instructions, and observations is a real asset.\n\nThe paper is clearly written and honest about its limitations. The execution section explicitly states that the RL slicing skill is assumed to return to a predefined ending pose, and the real-robot validation is qualitative. That honesty is good, but it does not make the assumption safe: if the skill drifts, subsequent stages fail, and the paper never quantifies skill reliability.\n\nThe soft spots are concentrated in the numbers. The abstract reports a 17.5% margin over baseline; the introduction says 18%; Section V-B says 10%. No confidence intervals, significance tests, or trial counts accompany the main comparison in Fig. 5 or Table I. For roughly 9-10 problems per task, a few trials could easily swing the aggregate by several points. This matters because the central claim is a quantitative superiority claim. The authors need to reconcile the values and provide the raw data.\n\nThe baseline deserves a comment: the \"VLM-as-a-planner\" directly generates action sequences, which is a weak reference; a stronger baseline like LLM+P that at least goes through PDDL would be fairer. But the CP comparison is done for both methods, so it's not a total strawman. The ablation on ICL is useful, showing a 5.2% gain for the full system and 10% without CP.\n\nThe stress-test note is on target. I don't see a circularity problem in the main comparison; the system is tested against an external baseline, not derived from the authors' prior work. The integration is new even if the components are known.\n\nBottom line: this paper is for researchers working on language-conditioned TAMP, especially bimanual manipulation. They'll find a practical template and a useful dataset. It deserves peer review, but only with the authors committing to release the exact trial counts and confidence intervals, and to reconcile the three different headline numbers.\n\nRecommendation: send to review, with a strong request for the missing experimental record.","headline":"Useful integration of VLM-to-PDDL generation with TAMP for bimanual cooking, but the main success-rate claim is not yet verifiable from the paper as written.","tokens_in":11657,"tokens_out":2407,"would_cite":true,"duration_ms":24359,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"Grounding vision-language output in symbolic-and-geometric planning with corrective failure feedback yields higher success than direct VLM planning on two-arm cooking tasks.","keywords":["bimanual manipulation","task and motion planning","vision-language models","PDDL problem generation","corrective planning","long-horizon manipulation","cooking robotics","failure feedback"],"falsifier":"Run the learned slicing skill in isolation over the same food types and poses used in the experiments and record how frequently it lands on the predefined ending pose; if that rate is low, the optimistic planning assumption would fail in practice and the real-robot success figures would not reflect typical deployments.","tokens_in":10643,"feed_emoji":"🤖","tokens_out":8404,"duration_ms":94356,"temperature":0.7,"pith_summary":"ViLaIn-TAMP tries to settle a practical question: can a robot turn a natural-language instruction and a camera image into a long-horizon, two-arm manipulation plan that is safe to execute? The paper's answer is to combine a vision-language model that writes structured symbolic planning problems with a planner that checks those problems for logical consistency and collision-free motions, and then closes the loop by feeding motion failures back to the model for revision. On five bimanual cooking tasks, the framework outperforms a direct vision-language-as-a-planner baseline by a reported 18 percentage points in mean success rate, with corrective planning adding a further 32-point boost. The reason this matters is that direct vision-language planners can hallucinate goals and produce plans that are physically infeasible; a symbolic check catches these before the robot moves.","feed_headline":"Hybrid robot planner beats direct vision-language plans by 18 points","feed_subtitle":"Symbolic checks plus corrective replanning lift success in two-arm cooking tasks.","key_machinery":"The load-bearing mechanism is a three-part cycle. First, a vision-language interpreter estimates objects, initial state, and goal from the instruction and image, writing them as a structured PDDL problem. Second, an integrated task-and-motion-planning module first finds a symbolic action sequence and then verifies and samples collision-free motions for it, treating learned contact-rich skills such as slicing as fixed start-to-end stages with enforced pose constraints. Third, a corrective-planning module takes the motion planner's detailed failure reports—collision partners, failed grasp, reachability—and re-prompts the interpreter with these constraints to produce a revised problem, repeating until success or a limit. The paper's key modification is making the motion planner emit structured, semantically meaningful failure feedback that a vision-language model can reason over, since raw planner scenes are not directly usable by such models.","core_discovery":"The paper's central claim is that grounding a vision-language interpreter in an integrated task-and-motion-planning loop yields verifiable, interpretable, and more successful bimanual manipulation than generating action plans directly from a vision-language model. In the proposed design, the interpreter converts a linguistic instruction and a scene image into a complete problem specification in the Planning Domain Definition Language (PDDL), a standardized symbolic planning format; a high-level symbolic planner then searches for an action sequence, and a multi-stage motion planner verifies that the sequence can be realized as collision-free trajectories, incorporating learned skills such as slicing as constrained planning stages. When verification fails, a corrective-planning module converts the motion failure into natural-language feedback, re-prompts the interpreter to revise the specification, and replans. Evaluated over five cooking tasks, the full system reaches higher success rates than the direct-planning baseline, especially as task complexity grows, and the corrective loop is shown to be a major source of the gain. The approach is also demonstrated on a real dual-arm robot.","pith_inferences":["Inference: the size of the corrective-planning gain suggests that a large share of VLM planning errors are motion-level geometric mistakes rather than pure language-understanding errors; if perception were perfect, the gap between direct planning and the hybrid loop might shrink but would not vanish, because the loop also corrects contradictory goal specifications.","Inference: the same failure-feedback loop could be ported to other contact-rich skills by training each skill with an explicit terminating pose and wrapping it as a constrained stage, extending the framework beyond slicing to pouring, kneading, or assembly.","Inference: a simple controlled experiment—feeding the same five problems to the framework with and without the corrective loop while holding the foundation model and perception fixed—would isolate how much of the gain comes from feedback quality versus model revision ability."],"forward_implications":["A robot can accept a new instruction and scene image and produce a plan that is checked for logical and physical feasibility before any motion is executed.","Two-arm tasks with inter-arm collision constraints no longer require a hand-tuned planner per task; the same domain and loop handles pick-and-place, obstacle avoidance, slicing, and serving.","When motion planning fails, the system does not simply stop: it converts the failure into a revised symbolic problem and attempts a new plan, which is what produces most of the reported success-rate gain.","Contact-rich skills can be plugged into the same framework as staged skills with enforced start and end poses, so adding new skills extends the planner without rewriting the symbolic domain.","Non-expert users could direct a dual-arm cooking robot by language while keeping the plan inspectable in a structured symbolic form before execution."],"supporting_citations":[{"why":"Supplies the Vision-Language Interpreter (ViLaIn) that the paper adapts to convert linguistic instructions and scene observations into PDDL problem specifications.","marker":"[15]"},{"why":"Provides the multi-stage manipulation planning framework that the paper modifies to emit structured motion failure feedback for corrective planning.","marker":"[19]"},{"why":"Provides the off-the-shelf symbolic planner used to search high-level action sequences from the generated PDDL problems.","marker":"[20]"},{"why":"Serves as the foundation model for the object, initial-state, and goal-state estimation modules and for corrective-planning revision.","marker":"[6]"},{"why":"Supplies the vision-language object detector used to localize and label task-relevant objects in the scene.","marker":"[22]"},{"why":"Supplies the reinforcement-learned slicing skill that is integrated as a mock planning stage with fixed start and end pose constraints.","marker":"[43]"},{"why":"Provides the segmentation model used in the real-robot perception pipeline to estimate object poses accurately during execution.","marker":"[47]"}],"fun_headline_variants":["Verifiable bimanual planner beats VLM baseline by 17.5%","Grounded interpreter yields safer two-arm robot plans","Hybrid planner adds corrective feedback for bimanual tasks","VLM plus symbolic checks lifts dual-arm success by 18 points","Interpretable bimanual planning with vision and corrective loops"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The plan's real-world success depends on the learned slicing skill reliably returning the robot to the predefined ending pose after every execution, and the paper does not measure how often that actually happens.","fun_headline_variants_meta":{"raw":{"variants":["Verifiable bimanual planner beats VLM baseline by 17.5%","Grounded interpreter yields safer two-arm robot plans","Hybrid planner adds corrective feedback for bimanual tasks","VLM plus symbolic checks lifts dual-arm success by 18 points","Interpretable bimanual planning with vision and corrective loops"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000489,"raw_usage":{"total_tokens":2459,"prompt_tokens":1052,"completion_tokens":1407,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":668,"completion_tokens_details":{"reasoning_tokens":1321}},"tokens_in":668,"tokens_out":1407,"duration_ms":11138,"temperature":1.0,"reasoning_tokens":1321,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-07T11:07:38.686093+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Run the learned slicing skill in isolation over the same food types and poses used in the experiments and record how frequently it lands on the predefined ending pose; if that rate is low, the optimistic planning assumption would fail in practice and the real-robot success figures would not reflect typical deployments.","supporting_citations":[{"cited_title":"Vision-language interpreter for robot task planning,","cited_arxiv_id":null,"evidence_quote":"Supplies the Vision-Language Interpreter (ViLaIn) that the paper adapts to convert linguistic instructions and scene observations into PDDL problem specifications."},{"cited_title":"MoveIt! Task Constructor for Task-Level Motion Planning,","cited_arxiv_id":null,"evidence_quote":"Provides the multi-stage manipulation planning framework that the paper modifies to emit structured motion failure feedback for corrective planning."},{"cited_title":"The fast downward planning system,","cited_arxiv_id":null,"evidence_quote":"Provides the off-the-shelf symbolic planner used to search high-level action sequences from the generated PDDL problems."},{"cited_title":"GPT-4o system card,","cited_arxiv_id":null,"evidence_quote":"Serves as the foundation model for the object, initial-state, and goal-state estimation modules and for corrective-planning revision."},{"cited_title":"Qwen2.5-VL technical report,","cited_arxiv_id":null,"evidence_quote":"Supplies the vision-language object detector used to localize and label task-relevant objects in the scene."},{"cited_title":"SliceIt! - a dual simulator framework for learning robot food slicing,","cited_arxiv_id":null,"evidence_quote":"Supplies the reinforcement-learned slicing skill that is integrated as a mock planning stage with fixed start and end pose constraints."},{"cited_title":"SAM 2: Segment anything in images and videos,","cited_arxiv_id":null,"evidence_quote":"Provides the segmentation model used in the real-robot perception pipeline to estimate object poses accurately during execution."}],"review_version":1}