liyang
ae28d55f81
Source: RGB-D -> Dyn-HaMR -> L20 retargeting -> FoundationPose -> reference repair -> SPIDER, documented in docs/PIPELINE_LATEST.md and docs/SETUP_AND_WEIGHTS.md. Adds FoundationPose and nvdiffrast upstream snapshots, requirements/pipeline_venv.txt and the FoundationPose weight manifest/downloader. Assets (Git LFS): weights/ (WiLoR detector, HandFlow denoiser, UniDepth-L), FoundationPose checkpoints, HaMeR checkpoint, Dyn-HaMR HMP model and BMC constraints, L20 URDF/meshes, the 20260915_171525 D405 recording and the two box CADs. MANO models are not redistributed (third_party/hamer/_DATA/data/mano/README.txt). Environments, caches and run outputs excluded. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
39 lines
2.6 KiB
Python
39 lines
2.6 KiB
Python
"""Actual upstream SPIDER, with L20 passive-joint reward indexing corrected locally."""
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import os,sys,json,importlib.util,argparse
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from pathlib import Path
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ROOT=Path(__file__).resolve().parents[1];OUT=Path(os.environ.get('SPIDER_TASK_OUT',str(ROOT/'output/foundationpose_spider_20260915')))
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os.environ['WARP_CACHE_PATH']=str(ROOT/'.spider_cache/warp');os.environ['TORCHINDUCTOR_CACHE_DIR']=str(ROOT/'.spider_cache/torchinductor');os.environ.setdefault('MUJOCO_GL','osmesa');os.environ['OMP_NUM_THREADS']='4'
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sys.path.insert(0,str(ROOT/'third_party/spider'))
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import torch
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torch.set_num_threads(4)
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from spider.config import Config
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import spider.simulators.mjwp as sim
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original=sim._weight_diff_qpos
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def weights(config):
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if config.embodiment_type!='bimanual':return original(config)
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# 27 state DOFs per L20 (6 wrist+21 finger), versus 22 actuator channels.
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half=(config.nv-12)//2;w=torch.full((config.nv,),config.joint_rew_scale,device=config.device)
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for start in [0,half]:w[start:start+3]=config.base_pos_rew_scale;w[start+3:start+6]=config.base_rot_rew_scale
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for start in [config.nv-12,config.nv-6]:w[start:start+3]=config.pos_rew_scale;w[start+3:start+6]=config.rot_rew_scale
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return w
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sim._weight_diff_qpos=weights
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spec=importlib.util.spec_from_file_location('spider_original_runner',ROOT/'third_party/spider/examples/run_mjwp.py');module=importlib.util.module_from_spec(spec);sys.modules[spec.name]=module;spec.loader.exec_module(module)
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# Cross-object regrasp invalidates upstream one-object-per-hand delta heuristic.
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# Retain reference object assistance schedule and per-finger contact reward.
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module.compute_contact_point_delta=lambda *args,**kwargs: None
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get_qpos_original=module.get_qpos
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def checked_qpos(config,env):
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q=get_qpos_original(config,env)
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if not torch.isfinite(q[0]).all():raise RuntimeError('Nonfinite executed state: stop instead of saving invalid trajectory')
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return q
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module.get_qpos=checked_qpos
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p=argparse.ArgumentParser();p.add_argument('--pilot',action='store_true');p.add_argument('--steps',type=int);args=p.parse_args();c=json.loads((OUT/'config.json').read_text())
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if args.pilot:c.update(max_sim_steps=80,num_samples=32,max_num_iterations=2)
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if args.steps:c['max_sim_steps']=args.steps
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if (OUT/'physics_parameters.json').exists():
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settings=json.loads((OUT/'physics_parameters.json').read_text());setup_original=sim.setup_mj_model
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def configured_model(config):
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m=setup_original(config);m.opt.iterations=settings['solver_iterations'];m.opt.ls_iterations=settings['ls_iterations'];return m
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sim.setup_mj_model=configured_model;module.setup_mj_model=configured_model
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module.main(Config(**c));print('SPIDER_RUN_COMPLETE',flush=True)
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