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WIP H1/H2: Newton actuator stiffness bump diagnostic
H1 result (stiffness 50/damping 5): mean reward 617.8 at iter 200, vs Newton baseline at stiffness 1.0/0.1 = 27.7, vs PhysX at stiffness 1.0 = 246.7. Conclusion: the Newton baseline 10x reward gap was caused by under-tuned actuator gains, NOT by the actuator-binding bug (Newton issue #2585). Wiring is functional; the per-DOF joint_target_ke/kd/pos arrays IsaacLab Newton writes to ARE consumed by MJWarp. Why the discrepancy with PhysX at the same nominal stiffness=1.0: - PhysX path layers fixed_tendons_props(limit_stiffness=30, damping=0.1) on top of the implicit drive — adds tendon-coupled torsional stiffness Newton's MjcTendon parsing does not contribute equivalently. - PhysX runs solver_position_iteration_count=8 per substep, amplifying effective stiffness; Newton's MJWarp implicit-PD step is single-pass. Current value 20.0/2.0 (H2) is the mid-range probe — still WIP.
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source/isaaclab_tasks/isaaclab_tasks/direct/shadow_hand_over/shadow_hand_over_env_cfg.py

Lines changed: 13 additions & 8 deletions
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@@ -180,17 +180,22 @@ def _newton_shadow_hand_cfg(
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"robot0_THJ(2|1)": 0.99,
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"robot0_THJ0": 0.81,
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},
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# H1 result: with stiffness 1.0 (matching PhysX cfg), Newton reaches only
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# ~27 mean reward at iter 200. With stiffness 50.0 / damping 5.0 it reaches
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# ~617 mean reward — well above PhysX's 247. Wiring is fine; baseline gains
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# are just severely under-tuned for Newton's MJWarp implicit-PD path.
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# H2: triangulate between to find a value that matches PhysX behavior.
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stiffness={
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"robot0_WRJ.*": 5.0,
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"robot0_(FF|MF|RF|LF|TH)J(3|2|1)": 1.0,
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"robot0_(LF|TH)J4": 1.0,
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"robot0_THJ0": 1.0,
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"robot0_WRJ.*": 20.0,
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"robot0_(FF|MF|RF|LF|TH)J(3|2|1)": 20.0,
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"robot0_(LF|TH)J4": 20.0,
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"robot0_THJ0": 20.0,
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},
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damping={
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"robot0_WRJ.*": 0.5,
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"robot0_(FF|MF|RF|LF|TH)J(3|2|1)": 0.1,
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"robot0_(LF|TH)J4": 0.1,
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"robot0_THJ0": 0.1,
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"robot0_WRJ.*": 2.0,
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"robot0_(FF|MF|RF|LF|TH)J(3|2|1)": 2.0,
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"robot0_(LF|TH)J4": 2.0,
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"robot0_THJ0": 2.0,
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},
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friction=1e-2,
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armature=2e-3,

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