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A decentralized approach using neural HJR learning achieves superior safety and efficiency in multi-arm motion planning, outperforming traditional methods.
Neural motion planners can now navigate complex, multi-room environments with significantly improved robustness by incorporating sparse roadmap guidance as weak supervision.
Coordinating multi-robot teams to complete manipulation tasks just got easier: GoC-MPC handles dynamic task assignments and disturbances without training data or environment models.
Steer your robot's diffusion policy away from failure modes at inference time with a lightweight performance predictor trained via self-supervised attention.
