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Forget end-to-end training: DexMulti's "retrieve-align-execute" approach lets robots master complex, multi-stage dexterous tasks from just a handful of demonstrations.
A portable IMU-based teleoperation system slashes the data requirements for humanoid robot manipulation by pre-training on human motion and then fine-tuning on robot data.
Forget scaling laws: this humanoid robot model crushes benchmarks using 10x less data by cleverly pre-training on human videos and then fine-tuning on robot-specific movements.
Robots get smarter at in-context learning by "thinking" visually about future trajectories, leading to better generalization and success rates in manipulation tasks.
