EmboTeam: Asymmetric State Grounding for Heterogeneous Multi-Robot Collaboration
Overview
This paper presents EmboTeam, a neuro-symbolic framework designed for asymmetric state grounding in heterogeneous multi-robot collaboration. The system uses an LLM to generate structured state expectations, which are then verified via symbolic search using PDDL and grounded into a team-level parallel behavior tree. Robot-specific subtrees handle precondition checking, skill execution, effect validation, local replanning, and blackboard-based synchronization. The authors evaluate the framework on MACE-THOR, a dataset containing 42 heterogeneous multi-robot tasks across 8 indoor layouts. Results show that EmboTeam improves task success rate from 12% to 55% and goal condition recall from 32% to 75% compared to LaMMA-P, demonstrating the effectiveness of synchronized reactive grounding for long-horizon collaboration.
Generated from attributed reports · Updated 3 hours ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · Reported success rate: 55 percent · reported trials · Differing source assertions
“oard-based synchronization. We further introduce MACE-THOR, con- taining 42 heterogeneous multi-robot tasks across 8 indoor layouts. EmboTeam improves task success rate from 12% to 55% and goal condition recall from 32% to 75% over LaMMA-P, demonstrating the effectiveness of synchronized reactive grounding for collaboration over long horizons.”
Exact source · revision 1Source owner not reported
Reported quantity · Reported recall: 75 percent · reported trials · Differing source assertions
“oard-based synchronization. We further introduce MACE-THOR, con- taining 42 heterogeneous multi-robot tasks across 8 indoor layouts. EmboTeam improves task success rate from 12% to 55% and goal condition recall from 32% to 75% over LaMMA-P, demonstrating the effectiveness of synchronized reactive grounding for collaboration over long horizons.”
Exact source · revision 1Source owner not reported
Reported quantity · Reported success rate: 12 percent · reported trials · Differing source assertions
“oard-based synchronization. We further introduce MACE-THOR, con- taining 42 heterogeneous multi-robot tasks across 8 indoor layouts. EmboTeam improves task success rate from 12% to 55% and goal condition recall from 32% to 75% over LaMMA-P, demonstrating the effectiveness of synchronized reactive grounding for collaboration over long horizons.”
Exact source · revision 1Source owner not reported
Reported quantity · Reported recall: 32 percent · reported trials · Differing source assertions
“oard-based synchronization. We further introduce MACE-THOR, con- taining 42 heterogeneous multi-robot tasks across 8 indoor layouts. EmboTeam improves task success rate from 12% to 55% and goal condition recall from 32% to 75% over LaMMA-P, demonstrating the effectiveness of synchronized reactive grounding for collaboration over long horizons.”
Exact source · revision 1Source owner not reported
Evidence dependency changes (1)
- 2026-11-09T22:31:09.646Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
Report timeline
Follow attributed reports and material updates.
- arXiv Robotics — research abstractsSignalEmboTeam: Asymmetric State Grounding for Heterogeneous Multi-Robot Collaboration
This paper presents EmboTeam, a neuro-symbolic framework designed for asymmetric state grounding in heterogeneous multi-robot collaboration. The system uses an LLM to generate structured state expectations, which are then verified via symbolic search using PDDL and grounded into a team-level parallel behavior tree. Robot-specific subtrees handle precondition checking, skill execution, effect validation, local replanning, and blackboard-based synchronization. The authors evaluate the framework on MACE-THOR, a dataset containing 42 heterogeneous multi-robot tasks across 8 indoor layouts. Results show that EmboTeam improves task success rate from 12% to 55% and goal condition recall from 32% to 75% compared to LaMMA-P, demonstrating the effectiveness of synchronized reactive grounding for long-horizon collaboration.
Timezone · UTC
Article dates follow your selected timezone. Briefing editions use Hong Kong time (UTC+8).