multi-modal-robot-motion-planning-stated-natural-language-hard-part CLM-0450mechanismsingle paperReviewed by AI
For multi-modal robot motion planning stated in natural language, the hard part for an LLM is synthesizing the cross-modal coupling (meeting points, contact locations, shared intermediate states), not the single-modality formulation: giving an LLM fixed expert single-modal MIP modules and asking it only to predict the interface variables gives 100% single-modal success but 55.6-63.2% multi-modal, below a model fine-tuned with solver-verified rewards to write the joint formulation (80.9%).
Capability: Planning a sequence of actions before acting
Observed on
Discretization-based approximate MIP formulations for non-convex robotic planning (UAV, IK, footstep, grasp), 1,316-problem benchmark, Qwen3-8B base plus SFT and GRPO; static envir.
Sources
- Validated by: Task success rate on 276 held-out LLMM3PBench problems versus prompt-only LLMs (GPT-5.2, Gemini 2.5 Flash, open models), PDDLStream, LLM+P, ReAct, sampling-based pipelines, and an Expert-MIP module-selection control; plus 40 real hardware scenarios at 87.5%. Reward ablations at matched 800-step budget show removing trajectory-correctness reward drops SR 84.8 to 50.3.. Measured on one benchmark authored by the same group, one base model (Qwen3-8B), three runs. The Expert-MIP control is the design element that isolates coupling from module quality; [truncated]
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Related claims
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- 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
- For LLM agents that must compose and then execute domain tool workflows, training separate adapters for the two roles splits the gains cleanly: supervised fine-tuning of the planner on reference tool sequences mainly improves workflow-order fidelity, while node-level RL on argument-match rewards mainly improves tool-argument grounding and final-answer quality, and combining both beats either on a 9B backbone.Planning a sequence of actions before acting · reviewed by AI
- When a multimodal LLM acts as the decision core of a simulated humanoid reacting to sudden household hazards, picking the correct reaction primitive does not mean the reaction is executable: even on correctly classified scenes, hands land a median 0.48 m from the true impact point, 89% of misses fall short, and in 49% of those decisions the body never steps forward and stands beyond arm's reach.Turning plans into physical action · reviewed by AI
- In VLM-driven human-aware robot task planning over scene graphs, the bottleneck is inferring and grounding the symbolic goal, not the planning: once both human and robot goals correctly cover the ground truth, a formal multi-agent planner produces a valid, conflict-free joint plan in 99.6% of trials, while end-to-end success with VLM-inferred human goals is 69.7%.Planning a sequence of actions before acting · 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: An LLM given fixed expert single-modal MIP modules and asked only to supply interface variables matches or beats an end-to-end solver-RL-trained model on multi-modal composition success on this or a comparable benchmark.