Hand shape: sign pose 5

You control MyoHand, MyoSuite's musculoskeletal model of a right human hand and forearm (MuJoCo physics), only through its 39 muscles. The forearm is fixed in space. 23 joints move: the wrist (pro_sup pronation/supination, deviation, flexion), the thumb (cmc_abduction, cmc_flexion, mp_flexion, ip_flexion) and the four fingers, numbered 2 = index, 3 = middle, 4 = ring, 5 = little, each with mcpN_flexion and mcpN_abduction (knuckle), pmN_flexion (middle joint) and mdN_flexion (end joint). Joint angles are in radians; robo info lists each joint's range under joint_ranges_rad. The muscles are the forearm muscles of the wrist and fingers (e.g. ECRL, FCU, FDS2..FDS5, FDP2..FDP5, EDC2..EDC5, FPL, EPL, APL) and the small hand muscles (OP, and per finger RI, LU_RB, UI_UB interossei/lumbricals). A muscle can only pull, many muscles cross several joints, and the fingers collide with each other (contacts), so moving one joint tends to move others. World frame in metres, +z up; the fingers point roughly along -y.

Task

Form hand shape 5 of the ten American Sign Language hand shapes MyoSuite defines (the env's ASL_qpos table). The hand starts open (all joints 0). Some target shapes press fingers together, so an exact match of every joint is neither needed nor always possible. Bring the joints to the target angles in target_pose_rad (MyoSuite env myoHandPose5Fixed-v0). The target is pro_sup = 0.000, deviation = 0.000, flexion = 0.000, cmc_abduction = 0.338, cmc_flexion = 0.253, mp_flexion = 0.016, ip_flexion = -0.026, mcp2_flexion = 0.646, mcp2_abduction = 0.011, pm2_flexion = 0.128, md2_flexion = 0.112, mcp3_flexion = 0.511, mcp3_abduction = 0.000, pm3_flexion = 0.377, md3_flexion = 0.118, mcp4_flexion = 0.306, mcp4_abduction = -0.011, pm4_flexion = 0.401, md4_flexion = 0.134, mcp5_flexion = 0.220, mcp5_abduction = -0.068, pm5_flexion = 0.275, md5_flexion = 0.016 rad. At the start the pose error norm is 1.20 rad.

Success: MyoSuite's own solved check for this env: the Euclidean norm of the joint-angle error over all 23 joints (target minus current, pose_error_norm) must be below 0.7 rad (threshold, the env's pose_thd), judged by the episode server from the simulated state after you call robo done and the 10-step settle. Simulated time advances only when you act (one step = 20 ms).

Controls. robo act E1 E2 ... [--repeat N] sets the excitation (neural drive) of every muscle, a number in [0, 1], in the order robo info lists them (the action names), and holds it for N steps of 20 ms. A muscle's activation follows its excitation with MuJoCo's first-order activation dynamics (about 10 ms rising, 40 ms falling); its force also depends on its length and speed. The excitations stay as you last sent them until you act again, and they are also what the muscles hold during the 10-step settle after robo done. Skills (robo info lists them): robo skill set_joint_targets JOINT=RAD [JOINT=RAD ...] [steps=60] [ramp=20] runs a muscle-space controller: every step it computes the joint torques of a PD law towards the targets (MuJoCo inverse dynamics), solves a bounded least-squares problem for the muscle activations in [0, 1] that best produce those torques, and sends the matching excitations as that step's action (the steps count against the budget like robo act). Joints you do not name keep their previous targets (at the start: the start pose). The targets move linearly from the current pose over RAMP steps; the skill stops after STEPS steps (at most 300) or once the joints are still, and reports the remaining joint-target error and the task's error. robo skill hold [steps=20] sets every target to the current angle and keeps the joints there with the same controller. The controller cannot do the impossible: joints coupled by shared muscles or blocked by contact can stop short of their targets, so check the result and adjust.

Observation. robo observe reports target_pose_rad (the goal angles), pose_error_norm and threshold; joint_angles_rad, joint_velocities_rad_s and joint_targets_rad (the controller's current targets) per joint; muscle_activations (one per muscle, in action order); contacts (pairs of bones or objects touching); time_s; and obs_vector, MyoSuite's own observation vector for this env (its observation keys, in order: qpos, qvel, pose_err, act; not printed by plain robo observe, shown with robo observe --json). robo observe --image saves a picture from a fixed camera (muscle paths are not drawn).

The step budget is 500 steps (10 s of simulated time).