MyoChallenge-2025 Documentations

Table Tennis Rally
Soccer Shootout

Table Tennis Rally

The agent must hit a pingpong ball such that the ball lands on the opponent’s side using a paddle. This task requires coordination of a ‘myoArm’ model and a ‘myoTorso’ model as to allow the agent to accurately hit the pingpong ball without missing and allowing enough force so that the ball reaches within the dimensions of the opponent’s side.

Objective

To develop a general policy to engage in a high-speed rally. Move the ball from the agent’s side to the opposite side by hitting the ball with a paddle.

Action Space

The action space includes three major parts, the myoArm, consisting of 63 muscles, the myoTorso, consisting of 210 muscles and two position actuators for pelvis translation in the x,y plane.

Observation Space

Description	Component	Count
Pelvis Position	pelvis_pos	3
Joint Positions	body_qpos	58
Joint Velocities	body_vel	58
Ball Position	ball_pos	3
Ball Velocity	ball_vel	3
Paddle Position	paddle_pos	3
Paddle Velocity	paddle_vel	3
Paddle Orientation	paddle_ori	3
Paddle Reaching Error (see below)	reach_err	3
Muscle Activations	muscle_activations	273
Touching Information (see below)	touching_info	6

Description of observations

The paddle reaching error measures the distance between the MPL and the object

The touching information indicates the pingpong ball’s contact with various objects in the environment:

Paddle: Whether the ball is in contact with the paddle.

Own: Whether the ball is in contact with the agent.

Opponent: Whether the ball is in contact with an opponent agent.

Ground: Whether the ball is in contact with the ground.

Net: Whether the ball is in contact with the net.

Env: Whether the ball is in contact with any part of the environment.

Ping Pong Object Properties

Object	Properties
Table Top (Total)	2.74 × 1.52 × 1.59 m³
Table Top (Each Side)	1.37 × 1.52 × 1.59 m³
Net	0.01 × 1.825 × 0.305 m³
Paddle Handle	Radius: 1.6 cm, Height: 5.1 cm
Paddle Face	Radius: 9.3 cm, Height: 2 cm
Paddle Mass	150 g
Ball Radius	2 cm
Ball Mass	2.7 g
Ball Inertia	7.2×10⁻⁷ kg·m²

Starting Criteria: Phase 1 `myoChallengeTableTennisP1-v0`

The ball starts with the same speed
The agent has the same starting position
The paddle initially starts in the grasping position with the hand, but is not connected.
The starting position of the ball is randomly reset between [-1.20, -0.45, 1.50] and [-1.25, -0.50, 1.40] in x, y, z direction respectively at the start of the episode.
Maximum time: 3 seconds

Starting Criteria: Phase 2 `myoChallengeTableTennisP2-v0` (Please note the exact evaluation would be hidden.)

The ball starts with a different velocity, guaranteed to land onthe model’s side of the table
The agent has the same starting position
The paddle initially starts in the grasping position with the hand, but is not connected.
Table Tennis Paddle: mass is randomized between 100g - 150g.
Table tennnis friction: a +- change between 0 - 0.1, 0 - 0.001, 0 - 0.00002 from nominal value: [1.0, 0.005, 0.0001] in respective geom direction in (myoChallengeTableTennisP1-v0)
The starting position of the ball is randomly reset between [-0.5, 0.50, 1.50] and [-1.25, -0.50, 1.40] in x, y, z direction respectively at the start of the episode.
Maximum time: 3 seconds

Success Criteria

The ball is hit by the paddle once and only once
The ball does not have contact with the agent’s side of the table
The ball hits the opponent’s side of the table

Ranking Criteria

Success rate of hitting the ball (successful_attempts / total_attempts)
Effort: based on muscle activation energy

Soccer Shootout

The locomotion task focuses on goal-scoring using dynamic muscular control. The agent must kick a soccer ball, such that it enter’s the goal net. This task requires coordination of a ‘myoLeg’ model and a ‘myoTorso’ model as to allow the agent to accurately hit the ball without missing and allowing enough force that the ball reaches within the confines of the net.

Objective

To develop policies that allow for coordinated locomotion and kicking of a ball to score goals in a net with and without a goalkeeper.

Action Space

The action space includes two major parts, the myoLeg, consiting of 80 leg muscles, and the myoTorso, consisting of 210 lumabr muscles.

Observation Space

Description	Component	Count
Ball Position	ball_pos	3
4 Position Coords (bounding goal area)	goal_bounds	12
Muscles Activations	act	290
Joint Angles	internal_qpos	46
Joint Velocities.	internal_qvel	46
Foot Position (Right)	r_toe_pos	3
Foot Position (Left)	l_toe_pos	3
Body COM in world frame	model_root_pos	7
Body COM vel in world frame	model_root_vel	6

Note: The body COM is represents with a freejoint, hence the 7 dimensions, in the form of [x, y, z, qx, qy, qz, qw] Similarly the body COM velocity is represented with 6 dimensions, in the form of [vx, vy, vz, alpha, beta, gamma]

Soccer Object Properties

Object	Properties
Soccer Net	Width: 7.32 m, Height: 2.50 m
Ball Radius	0.117m
Ball Mass	450g

Starting Criteria: Phase 1

The soccer ball is in a fixed location
The starting position of the agent is randomly reset between [39, -1.0] and [38, 1.0] in [x, y] direction at the start of the episode.
The agent is always placed in front of the ball, facing the goal.
Maximum time: 20 seconds

Starting Criteria: Phase 2

The ball is placed in a fixed starting location.
The starting position of the agent is randomly reset between [39, -2.0] and [37, 2.0] in [x, y] direction at the start of the episode. Note the increase in randomization distance as compared to Phase 1.
The agent is always placed in front of the ball, facing the goal.
Goalkeeper is now active, choosing between 3 movement strategies: Stationary, Random movement and Track ball
Velocity of goalkeeper will also be randomized at each episode
Maximum time: 10 seconds

Success Criteria

The soccer ball is fully within the confines of the net.

Ranking Criteria

Success rate of scoring goals (goals_scored / total_attemps)
Effort: based on muscle activation energy

Challenge Tutorial

This section aims to provide an basics to get start of the challenge.

For a step-by-step tutorial, please check our Tutorials page Using Reinforcement Learning and RL Baselines page. To obtain a more in-depth understanding of the challenge, we have prepared baselines for both of the challenges.

from myosuite.utils import gym
# Include the table tennis track environment, uncomment to select the soccer track challenge
# env = gym.make('myoChallengeSoccerP1-v0')
env = gym.make('myoChallengeTableTennisP1-v0')


env.reset()

# Repeat 1000 time steps
for _ in range(1000):

    # Activate mujoco rendering window
    env.mj_render()

    # Select skin group
    geom_1_indices = np.where(env.mj_model.geom_group == 1)
    # Change the alpha value to make it transparent
    env.mj_model.geom_rgba[geom_1_indices, 3] = 0


    # Get observation from the environment, details are described in the above docs
    obs = env.get_obs()
    current_time = obs['time']
    #print(current_time)


    # Take random actions
    action = env.action_space.sample()


    # Environment provides feedback on action
    next_obs, reward, terminated, truncated, info = env.step(action)


    # Reset training if env is terminated
    if terminated:
        next_obs, info = env.reset()

MyoChallenge-2025 Documentations

Table Tennis Rally

Objective

Action Space

Observation Space

Starting Criteria: Phase 1 myoChallengeTableTennisP1-v0

Starting Criteria: Phase 2 myoChallengeTableTennisP2-v0 (Please note the exact evaluation would be hidden.)

Success Criteria

Ranking Criteria

Soccer Shootout

Objective

Action Space

Observation Space

Starting Criteria: Phase 1

Starting Criteria: Phase 2

Success Criteria

Ranking Criteria

Challenge Tutorial

Starting Criteria: Phase 1 `myoChallengeTableTennisP1-v0`

Starting Criteria: Phase 2 `myoChallengeTableTennisP2-v0` (Please note the exact evaluation would be hidden.)