aiqbench analysis
This research proposes a novel approach to long-horizon robotic manipulation by leveraging overlap-induced multimodality in action distributions, eliminating the need for explicit behavior sequencing. By utilizing a shared Flow Matching policy and critic-guided planning, the method enables autonomous coordination of sub-tasks directly from demonstrations without requiring identity labels or task-ordering supervision. Experimental results on both simulation and real robots demonstrate robustness across varying conditions, suggesting a significant step toward more flexible and scalable robotic skill acquisition.
Implicit Behavior Coordination from Sub-Task Demonstrations by Exploiting Overlap-Induced Multimodality
arXiv:2607.09234v2 Announce Type: replace Abstract: Long-horizon robotic rearrangement is commonly formulated as a skill-sequencing problem, where distinct behaviors are explicitly represented and coordinated by a planner or high-level policy. We investigate whether such explicit behavior identities and sequencing interfaces are necessary at all. We introduce implicit behavior coordination from sub-task demonstrations, where separately collected behaviors are coordinated without behavior identity labels, complete-task demonstrations, or task-ordering supervision. Our key observation is that overlap between sub-task demonstrations induces multimodal action distributions that need not be resolved through explicit behavior partitioning. Instead, this overlap-induced multimodality can be exploited as a coordination resource. We instantiate this idea with a shared Flow Matching policy that preserves multiple action modes and critic-guided in-sample planning that propagates task value across demonstrations and selects task-relevant modes. Experiments in Habitat and on a real robot show that implicit behavior coordination remains effective under reduced cross-behavior overlap, larger behavior mixtures, longer horizons, and execution failures, supporting the idea that long-horizon coordination can emerge directly from sub-task demonstrations without explicitly recovering or sequencing behavior identities.
Abstract:Long-horizon robotic rearrangement is commonly formulated as a skill-sequencing problem, where distinct behaviors are explicitly represented and coordinated by a planner or high-level policy. We investigate whether such explicit behavior identities and sequencing interfaces are necessary at all. We introduce implicit behavior coordination from sub-task demonstrations, where separately collected behaviors are coordinated without behavior identity labels, complete-task demonstrations, or task-ordering supervision. Our key observation is that overlap between sub-task demonstrations induces multimodal action distributions that need not be resolved through explicit behavior partitioning. Instead, this overlap-induced multimodality can be exploited as a coordination resource. We instantiate this idea with a shared Flow Matching policy that preserves multiple action modes and critic-guided in-sample planning that propagates task value across demonstrations and selects task-relevant modes. Experiments in Habitat and on a real robot show that implicit behavior coordination remains effective under reduced cross-behavior overlap, larger behavior mixtures, longer horizons, and execution failures, supporting the idea that long-horizon coordination can emerge directly from sub-task demonstrations without explicitly recovering or sequencing behavior identities.
Robotics (cs.RO)
arXiv:2607.09234 [cs.RO]
(or arXiv:2607.09234v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.09234 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 260 words
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