multi-step-assembly-task-planning-action-history-workstation-image-prompting CLM-0452mechanismsingle paperReviewed by AI
For multi-step assembly task planning from an action history and a workstation image, prompting a multimodal LLM to emit a structured (JSON) state representation and propagate it through each predicted action — then truncating the plan at the predicted state closest to the inferred goal — raises action executability and lowers final-state error relative to predicting actions alone, at the cost of shorter plans (lower overlap with recorded human action sequences) and roughly eight times the latency.
Capability: Planning a sequence of actions before acting
Observed on
Simulated two-arm block-assembly dataset (495 instances, 8 target structures), GPT-4o family models prompted in-context; no environment interaction or ground-truth initial state; b.
Sources
- supportsSTEP: State-Aware Task Estimation and Planning with Multi-Modal LLMs for Human-Robot CollaborationValidated by: Compared against an AntGPT-style task-estimation-plus-action-prediction baseline with no state information on the same inputs: +32.8% action executability and -14.8% final-state error, at 30/50/70/90% task-completion splits; replicated across five OpenAI models on a 45-instance subset (STEP better on 4 of 5); LCS was lower than baseline and runtime rose from ~18s to ~148s.. Measured on one simulated block-assembly dataset against a single reimplemented baseline; [truncated]
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Related claims
- For long-horizon embodied task planning where a symbolic model defines action preconditions and effects, correcting a violated plan by edits the action model itself implies (inserting the missing navigate or open) raises plan success more per LLM call than returning the violation to the LLM for replanning, and the gain persists for frontier models as well as compact ones.Planning a sequence of actions before acting · reviewed by AI
- In embodied multi-agent household planning with a fixed high-level skill interface, a team's task success rate overstates coordination quality, because goal predicates can be reached through illegal shortcuts: several of 11 tested MLLMs succeed on more episodes than they produce legal plans on (Claude Haiku 4.5 63.1% success vs 47.2% legal; Qwen3.6-Plus 69.1% vs 56.7%), while others honour constraints yet miss the goal.Getting more from several models than from one · reviewed by AI
- For LLM-driven robot manipulation where every tool call is schema-validated and every motion kinematically and collision-checked before execution, execution-time recovery mechanisms (retrying a failed plan with an alternative candidate, resetting to a collision-checked safe pose between stages) contribute more to end-to-end task success than canonical action sequencing, because most residual failures are transient planning or IK failures rather than wrong orderings.Turning plans into physical action · reviewed by AI
- When a pretrained vision-language-action policy is fine-tuned sequentially on a stream of real-robot manipulation tasks, the choice of action-normalization statistics is itself a determinant of continual-learning success: statistics that are task-derived, first-task-derived, or recomputed each stage produce forgetting or poor transfer, while statistics estimated once from a small task-independent calibration set covering the embodiment's motion range and then frozen give the best acquisition, retention and task-order stability.Turning plans into physical action · reviewed by AI
- For post-training a pretrained vision-language-action manipulation policy under a fixed per-round frame budget, collecting handheld demonstrations only at states where the human action chunk diverges from the policy's sampled action distribution, and weighting them by a learned task-progress advantage, raises task progress more than collecting the same number of frames of ordinary demonstrations and fine-tuning on them.Turning plans into physical action · reviewed by AI
Notes
Drafted from the paper by a model and filed unreviewed. Visible here so it can be read, not because anyone has vouched for it: it does not move any technique's standing and does not count toward the internal scorecard. Drafted confidence: medium. Falsifier as drafted: A comparison on the same or similar assembly planning data where adding structured state estimation and goal-distance truncation does not improve executability or final-state error over action-only prediction, once task-estimation accuracy is matched.