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The encoder_bias DR event was applied but the actor observed true joint positions, making the bias invisible to the policy during training.
peak_heights was never cleared on episode reset, so stale values from the previous episode carried over and corrupted the metric.
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louislelay
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| cfg.sim.njmax = 200 | ||
| cfg.sim.nconmax = 30 |
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Wow, such a low njmax/nconmax work?
| weight=-0.1, | ||
| params={"sensor_name": shank_ground_cfg.name}, | ||
| ) | ||
| cfg.rewards["thigh_collision"] = RewardTermCfg( |
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WDYT about an alive reward? I was thinking it could be helpful especially for rough terrain
When a foot penetrates slightly below the terrain surface, all downward rays miss and the sensor reported max_distance instead of ~0. This corrupted foot clearance rewards and caused heel-walking gaits. Use frame_z clamped to [0, max_distance] as the fallback for all-miss frames instead of unconditionally returning max_distance.
Create Mjlab-Velocity-Flat-Run-Unitree-G1 task that extends the flat config with a velocity command curriculum for learning to run. Move the curriculum out of the base velocity config where it was applied to all robots. Set Go1 flat iterations to 1.5k.
Increase base ang_vel_z from (-0.5, 0.5) to (-1.0, 1.0). Ramp the G1 run curriculum stages to (-1.5, 1.5) and (-2.0, 2.0).
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WIP