ShadowHandOpenAI_FF.yaml

# used to create the object
name: ShadowHand

physics_engine: ${..physics_engine}

# if given, will override the device setting in gym.
env: 
  numEnvs: ${resolve_default:16384,${...num_envs}}
  envSpacing: 0.75
  episodeLength: 160 # Not used, but would be 8 sec if resetTime is not set
  resetTime: 8 # Max time till reset, in seconds, if a goal wasn't achieved. Will overwrite the episodeLength if is > 0.
  enableDebugVis: False
  aggregateMode: 1

  clipObservations: 5.0
  clipActions: 1.0

  stiffnessScale: 1.0
  forceLimitScale: 1.0
  useRelativeControl: False
  dofSpeedScale: 20.0
  actionsMovingAverage: 0.3
  controlFrequencyInv: 3 # 20 Hz

  startPositionNoise: 0.01
  startRotationNoise: 0.0

  resetPositionNoise: 0.01
  resetRotationNoise: 0.0
  resetDofPosRandomInterval: 0.2
  resetDofVelRandomInterval: 0.0

  # Random forces applied to the object
  forceScale: 1.0
  forceProbRange: [0.001, 0.1]
  forceDecay: 0.99
  forceDecayInterval: 0.08

  distRewardScale: -10.0
  rotRewardScale: 1.0
  rotEps: 0.1
  actionPenaltyScale: -0.0002
  reachGoalBonus: 250
  fallDistance: 0.24
  fallPenalty: -50.0

  objectType: "block" # can be block, egg or pen
  observationType: "openai" # can be "openai", "full_no_vel", "full","full_state"
  asymmetric_observations: True
  successTolerance: 0.4
  printNumSuccesses: False
  maxConsecutiveSuccesses: 50
  averFactor: 0.1 # running mean factor for consecutive successes calculation

  asset:
    assetRoot: "../assets"
    assetFileName: "mjcf/open_ai_assets/hand/shadow_hand.xml"
    assetFileNameBlock: "urdf/objects/cube_multicolor.urdf"
    assetFileNameEgg: "mjcf/open_ai_assets/hand/egg.xml"
    assetFileNamePen: "mjcf/open_ai_assets/hand/pen.xml"

  # set to True if you use camera sensors in the environment
  enableCameraSensors: False

task:
  randomize: True
  randomization_params:
    frequency: 720   # Define how many simulation steps between generating new randomizations
    observations:
      range: [0, .002] # range for the white noise
      range_correlated: [0, .001 ] # range for correlated noise, refreshed with freq `frequency`
      operation: "additive"
      distribution: "gaussian"
      # schedule: "linear"   # "constant" is to turn on noise after `schedule_steps` num steps
      # schedule_steps: 40000
    actions:
      range: [0., .05]
      range_correlated: [0, .015] # range for correlated noise, refreshed with freq `frequency`
      operation: "additive"
      distribution: "gaussian"
      # schedule: "linear"  # "linear" will linearly interpolate between no rand and max rand
      # schedule_steps: 40000
    sim_params: 
      gravity:
        range: [0, 0.4]
        operation: "additive"
        distribution: "gaussian"
        # schedule: "linear"  # "linear" will linearly interpolate between no rand and max rand
        # schedule_steps: 40000
    actor_params:
      hand:
        color: True
        tendon_properties:
          damping:
            range: [0.3, 3.0]
            operation: "scaling"
            distribution: "loguniform"
            # schedule: "linear"  # "linear" will scale the current random sample by `min(current num steps, schedule_steps) / schedule_steps`
            # schedule_steps: 30000
          stiffness:
            range: [0.75, 1.5]
            operation: "scaling"
            distribution: "loguniform"
            # schedule: "linear"  # "linear" will scale the current random sample by `min(current num steps, schedule_steps) / schedule_steps`
            # schedule_steps: 30000
        dof_properties:
          damping: 
            range: [0.3, 3.0]
            operation: "scaling"
            distribution: "loguniform"
            # schedule: "linear"  # "linear" will scale the current random sample by `min(current num steps, schedule_steps) / schedule_steps`
            # schedule_steps: 30000
          stiffness: 
            range: [0.75, 1.5]
            operation: "scaling"
            distribution: "loguniform"
            # schedule: "linear"  # "linear" will scale the current random sample by `min(current num steps, schedule_steps) / schedule_steps`
            # schedule_steps: 30000
          lower:
            range: [0, 0.01]
            operation: "additive"
            distribution: "gaussian"
            # schedule: "linear"  # "linear" will scale the current random sample by `min(current num steps, schedule_steps) / schedule_steps`
            # schedule_steps: 30000
          upper:
            range: [0, 0.01]
            operation: "additive"
            distribution: "gaussian"
            # schedule: "linear"  # "linear" will scale the current random sample by `min(current num steps, schedule_steps) / schedule_steps`
            # schedule_steps: 30000
        rigid_body_properties:
          mass: 
            range: [0.5, 1.5]
            operation: "scaling"
            distribution: "uniform"
            setup_only: True # Property will only be randomized once before simulation is started. See Domain Randomization Documentation for more info.
            # schedule: "linear"  # "linear" will scale the current random sample by `min(current num steps, schedule_steps) / schedule_steps`
            # schedule_steps: 30000
        rigid_shape_properties:
          friction: 
            num_buckets: 250
            range: [0.7, 1.3]
            operation: "scaling"
            distribution: "uniform"
            # schedule: "linear"  # "linear" will scale the current random sample by `min(current num steps, schedule_steps) / schedule_steps`
            # schedule_steps: 30000
      object:
        scale:
          range: [0.95, 1.05]
          operation: "scaling"
          distribution: "uniform"
          setup_only: True # Property will only be randomized once before simulation is started. See Domain Randomization Documentation for more info.
          # schedule: "linear"  # "linear" will scale the current random sample by ``min(current num steps, schedule_steps) / schedule_steps`
          # schedule_steps: 30000
        rigid_body_properties:
          mass: 
            range: [0.5, 1.5]
            operation: "scaling"
            distribution: "uniform"
            setup_only: True # Property will only be randomized once before simulation is started. See Domain Randomization Documentation for more info.
            # schedule: "linear"  # "linear" will scale the current random sample by ``min(current num steps, schedule_steps) / schedule_steps`
            # schedule_steps: 30000
        rigid_shape_properties:
          friction:
            num_buckets: 250
            range: [0.7, 1.3]
            operation: "scaling"
            distribution: "uniform"
            # schedule: "linear"  # "linear" will scale the current random sample by `min(current num steps, schedule_steps) / schedule_steps`
            # schedule_steps: 30000

sim:
  dt: 0.01667 # 1/60
  substeps: 2
  up_axis: "z"
  use_gpu_pipeline: ${eq:${...pipeline},"gpu"}
  gravity: [0.0, 0.0, -9.81]
  physx:
    num_threads: ${....num_threads}
    solver_type: ${....solver_type}
    use_gpu: ${contains:"cuda",${....sim_device}} # set to False to run on CPU
    num_position_iterations: 8
    num_velocity_iterations: 0
    max_gpu_contact_pairs: 8388608 # 8*1024*1024
    num_subscenes: ${....num_subscenes}
    contact_offset: 0.002
    rest_offset: 0.0
    bounce_threshold_velocity: 0.2
    max_depenetration_velocity: 1000.0
    default_buffer_size_multiplier: 5.0
    contact_collection: 0 # 0: CC_NEVER (don't collect contact info), 1: CC_LAST_SUBSTEP (collect only contacts on last substep), 2: CC_ALL_SUBSTEPS (default - all contacts)