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intd-so101-bimanual – AI Model by leesangoh | AlphaNeural AI
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intd-so101-bimanual
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safetensors
Gr00tN1d7
robotics
vla
gr00t
so101
bimanual
nvidia/GR00T-N1.7-3B
finetune
other
us
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IntD SO-101 Bimanual (GR00T-N1.7-3B + Intent Distillation)
Real-world bimanual SO-101 policy from
"Act with Intent: Brain-to-Spine Intention Distillation"
(INDI). GR00T-N1.7-3B fine-tuned with the IntD grafted intent stream ([V*;R*] distilled from a frozen Cosmos-Reason2 teacher), 60k steps (GBS 129) on 400 teleop demos (4 tasks × 100: threading, basket nesting, cross-bin stacking, drawer storage; head + left/right wrist cameras).
Action space:
relative arm / absolute gripper
joint actions (official GR00T SO-100 convention), chunk 32, executed with receding horizon 8.
Serve with the
intd-vla
fork (
gr00t/eval/run_gr00t_server.py --embodiment-tag SO101_BIMANUAL
, α=1).
Real-world results (n=25 per cell, 600 paired rollouts)
Condition
Baseline
+IntD
In-distribution clean
71
76
Held-out objects
62
67
Distractors
53
60
Largest gains on precision/long-horizon tasks (drawer storage grasp +24pp). Baseline pair:
leesangoh/intd-so101-bimanual-baseline
.
