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Planner-Conditioned Diffusion Policy enables agents to generate diverse, non-redundant trajectories, achieving perfect success rates while enhancing travel efficiency and coordination.
SJRL not only overcomes collision challenges in multi-agent navigation but also adapts dynamically to real-world constraints, outperforming traditional methods in complex environments.
PRIMAL3 achieves unprecedented scalability in multi-agent pathfinding, successfully coordinating 100,000 agents while navigating complex environments.
Large-scale human motion data can now be effectively repurposed to teach diverse non-humanoid robots, unlocking new capabilities in locomotion and manipulation.
Neural solvers can now effectively handle the complexities of multi-agent coordination and multi-objective trade-offs in routing problems, outperforming traditional heuristics.
