Reach a point with the left hand
humanoidbench-reachhumanoidbenchUnitree H1 humanoid with two Shadow handsManipulationmedium
Instruction
You are the policy of a Unitree H1 humanoid robot (about 1.8 m tall, 5-joint legs, 4-joint arms) with a Shadow dexterous hand on each arm, simulated in MuJoCo by HumanoidBench (the h1hand robot). The robot is free-standing: it must keep its balance and it can fall. A walking controller keeps it balanced for you: it is Unitree's pretrained H1 locomotion policy, which drives the legs from a velocity command and steps in place when the command is zero. World frame in metres, +z up, the floor at z = 0; heading_deg is the robot's facing direction (0 = +x, counter-clockwise positive). The robot starts at the origin facing +x. Simulated time advances only when you act; the scene is paused while you think.
Task
Put the left hand on the target point scene.target and keep it there (scene. is the distance from the left hand to it). HumanoidBench's reach reward per step is 5 x torso uprightness (5 when upright) + 5 while the left hand is within 1 m of the target + 10 while it is within 5 cm of it, minus a tiny penalty on joint speeds: at most about 20 per step. Only the left hand counts.
Success: HumanoidBench scores every 20 ms step with the task's reward (at most about 20 per step for this task; the terms are in progress.reward_terms) and counts the task as solved when the episode return reaches its success bar of 12000. The episode ends as solved the moment progress.return reaches 12000; calling robo done earlier scores 0. This task has no fall termination, but a fallen robot cannot keep its hand on the target.
Controls. robo act FORWARD LEFT TURN LDX LDY LDZ RDX RDY RDZ [--repeat N] applies one 20 ms control step N times (N <= 50). FORWARD in [-1, 1] m/s and LEFT in [-0.5, 0.5] m/s are the walking velocity in the robot's heading frame, TURN in [-1, 1] rad/s the turn rate (+ = counter-clockwise). The robot reaches a new velocity within about half a second; at FORWARD 1 it walks at about 0.9 m/s, and the walking policy treats sideways-plus-forward speeds below about 0.2 m/s as zero. An all-zero FORWARD LEFT TURN holds the robot's current spot and heading: it keeps stepping in place and walks back when it drifts more than about 4 cm. LDX LDY LDZ and RDX RDY RDZ in [-1, 1] move the goal point of the left and right hand by 2 cm per step in the heading frame (x forward, y left, z up, origin on the floor under the pelvis); inverse kinematics on the 4 arm joints makes the hand follow its goal, so the hands move with the body when it walks. Zero holds the goal. The arms are about 0.64 m long from the shoulder (at about 1.48 m height, 0.2 m to each side of the pelvis), so a hand reaches about 0.55 m ahead of the pelvis at 1 m height; the fingers and wrists hold their start pose. Skills (robo info lists their arguments): walk_to X Y [HEADING_DEG] [TOL] walks to a floor point (it does not avoid obstacles), walk FORWARD LEFT TURN STEPS holds a velocity command, turn_to HEADING_DEG, stand STEPS holds the spot, reach left|right X Y Z [TOL] moves a hand to a world point and keeps it there while the body sways, hands_home, and sit_down (one-way: stops the walking controller and sits down where the robot stands, the pelvis ending about 0.2 m further back).
Observation. robo observe reports robot (pelvis position, heading_deg, head_height, torso_upright (1 = upright), com_velocity (world x, y) and com_velocity_body (forward, left), the current velocity command, and per hand pos (world), pos_body and goal_body (heading frame) and anchored_at (the world point a reach holds), fallen), scene (the task's objects and goals) and progress: HumanoidBench's return so far, last_reward and its reward_terms, steps of episode_steps, the success_bar for return-based tasks and solved. Camera images: robo observe --image (HumanoidBench's view for the task, following the robot).
The step budget is 1000 steps (20 s of simulated time), HumanoidBench's episode length for this task.
How the robot is controlled and scored
You are controlling a simulated robot. Read the task below, then solve it by running the robo command in your shell (start with robo info and robo observe). Keep going until the task is done, then call robo done once. Do not stop to ask questions; there is no human to answer.
How to control the robot
You are the robot's policy. You act only through the robo command in your shell. There is no other way to move the robot, and you cannot read or change the simulator, the scoring, or other files to succeed; the episode server judges the final physical state itself.
robo info # the robot, its sensors, action groups, skills and step budget
robo observe # robot and scene state as numbers
robo observe --image [--camera C] # also saves a camera image and prints its path (open it to look)
robo act V1 V2 ... [--repeat N] # one low-level action (the action groups under Controls), applied N times (N <= 50)
robo skill NAME ARG ... # run a skill listed by `robo info`; it runs until it finishes and reports the result
robo done "short summary" # end the episode and ask for scoring
robo give-up "reason" # end the episode without claiming success- Positions are in metres in the world frame (+z up); angles are in degrees unless a field says otherwise.
- The episode has a fixed step budget (see
robo info); every simulated control step counts, including the steps a skill runs. - Skills are ordinary controllers: they can fail, stop early or be blocked by the scene. Read what they report and re-observe.
- The episode ends as solved the moment the task's success rule holds (see Success);
robo donebefore that scores 0. - Call
robo doneexactly once when finished.
Run this task
ROBOUSE_ORACLE_TOKEN=$(openssl rand -hex 16) \
bench eval run \
-d carlosferrazza/humanoid-bench@0.2 \
--registry https://robouse.ai/hub/registry.json \
--agent oracle \
--include humanoidbench-reachPinned to robohub commit e472b1a1e041. The verifier and the reference solution are not published.