language-conditioned-manipulation-tasks-where-same-base-task-instantiated-increasing CLM-0237observationsingle paperReviewed by AI
On language-conditioned manipulation tasks where the same base task is instantiated at increasing levels of spatial ambiguity and procedural length, current VLA models degrade on fine-grained spatial relations, precise low-level execution, and memory-dependent multi-step planning, so success measured only on predefined simple settings overstates their competence.
Capability: Turning plans into physical action
Observed on
Representative VLA models evaluated on a 56-base-task, 280-variant benchmark across multiple embodiments; degradation is shown along authored difficulty levels, not in deployment..
Sources
- Measured on a new benchmark of 280 task variants with 527K collected trajectories and diagnostic metrics beyond binary success; abstract reports the qualitative direction of failures but no numbers are given in the source text, and the model set is described only as 'representative'.
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Related claims
- When language-conditioned VLA manipulation tasks are instantiated at graded difficulty levels that add spatial ambiguity (fine-grained spatial relations, precise placement) and procedural length (multi-stage plans requiring memory of completed steps), representative VLA models degrade on both axes, so success on predefined single-stage settings does not indicate spatial or long-horizon competence.Turning plans into physical action · reviewed by AI
- When adapting a pretrained VLA manipulation policy with only a handful of target demonstrations, retrieving prior demonstrations at the level of LLM-decomposed subtasks (then reranking by behavior-feature similarity) raises success more than retrieval by visual, optical-flow, or state-action similarity, and the margin is concentrated on long-horizon composite tasks rather than single-skill pick-and-place.Turning plans into physical action · reviewed by AI
- For frozen VLA policies executing repetitive, perceptually aliased manipulation tasks (counting pick-and-place), a progress memory that advances after each attempted action converts transient grasp or placement failures into persistent task-state errors, so writes must be gated on observed physical evidence of the intended transition; gating this way raised success far above the frozen policy and above memory-augmented baselines.Turning plans into physical action · reviewed by AI
- For deciding when a robot skill has finished and the next subtask should begin, general-purpose VLMs prompted zero-shot are near useless on long-horizon mobile manipulation (under 4% of transitions detected on BEHAVIOR-1K, including a frontier proprietary model), while a sub-1B student fine-tuned on compact self-explanations condensed from teacher reasoning over annotated demonstrations detects about 90% and lifts closed-loop task success from 0.30 to 0.53 against a 0.63 oracle.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
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 VLA model whose success rate and diagnostic metrics stay flat across the five difficulty levels, including the longest-horizon and most spatially ambiguous variants.