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Joint image-depth generation can be achieved with a single model trained on sparse data, outperforming existing methods by a significant margin.
AHEAD transforms static VLA models into dynamic manipulators, achieving up to 97% success in scenarios where traditional methods fail spectacularly.
Forget expensive tactile sensors – microphones and a lightweight CNN can give you accurate, real-time in-hand slip estimation.
Soft robots can now mimic human manipulation skills more effectively by explicitly reasoning about contact forces, outperforming traditional kinematic imitation and learning-based methods.
Robots can now learn manipulation skills from ordinary human videos, thanks to a 3D point tracking method that bridges the embodiment gap and requires only 20 robot demonstrations.
