Plan of Approach

Create a machine learning agent capable of reaching a certain height on a wall.

Some resources:
Magnus shocked by world’s most advanced climbing AI
AI vs. Stairs (deep reinforcement learning). While not necessarily related to bouldering, this video shows a machine learning agent that's put in a ragdoll, so perhaps it's to some use to me.

Plan of Approach segments

What does the AI look like, and what is it capable of?

• Some way to grab and hold on to the wall

  • Hands & feet - sensor in each
    • Unity ragdolls?
  • Colliders on the wall

• A way to detect where grabbable points are

  • Raycasts

• The AI can exert force from its hands & feet, to move towards a position.

How are rewards/punishments handled?

• A reward when:
  • it manages to reach a new record height
  • it grabs a stone which has a position above the agents height
  • it has no exhaustion (explained below)
• Some sort of "exhaustion" parameter
  • This should encourage trying to gain stability; holding onto the wall with as many body parts as possible (so preferably both hands and feet)
  • Hanging on to a wall with less than 4 body parts will gradually start to fill up the exhaustion meter.
  • When the exhaustion meter reaches 100, the agent gives up and falls down.
  • Exhaustion grows when the agent tries to counter gravity
    • When the wall is slightly inclined, the agent is able to rest and decrease its exhaustion meter. * However, having an exhaustion meter at 0 will result in punishment. This should prevent the agent from being too comfortable in its spot.
    • When the wall is straight up (no angle), there is no difference in exhaustion acceleration.
    • When the wall is slightly declined, the agent has to make use of all his "muscles" to hold on.
• A punishment when a condition is not satisfied
  • When it touches the ground
  • Becoming "exhausted"

What does the testing environment look like?

• A level
  • Increasing difficulty
  • Should be scalable and yet diverse (randomness implementation)
    • Procedural generation?
      • A wall prefab. Contains script that depending on selected difficulty, places x amount of grabbing stones for every x unit on the wall.
      • Grabbing stone prefabs. Are classed per difficulty.

Wall difficulties

1. Beginner
2. Intermediate
3. Strong climber
4. Expert
5. Boulderman

Let's define what each difficulty looks like.

1. Beginner

• Basic wall structure. Short height
• Plenty of grabbing points to hold on to - 100%
• Slight wall incline. This allows for more stability and lets the agent rest, decreasing the exhaustion meter.

2. Intermediate

• Longer wall height.
• A bit less grabbing points - 80%
• No wall incline.

3. Strong climber

• Even longer wall height
• Rotating wall (cylinder shape)
• Even less grabbing points - 60%
• Wall overhangs

4. Expert

• Collectibles on the climbing course: incentive for riskier climbs
• Even less grabbing points - 50%

5. Boulderman

• Time pressure
• Rocks falling down
• Collectibles on the climbing course: incentive for riskier climbs
• Very little grabbing points - 30%