Humanoid Robot Sim2Real: Algorithm Design

Objective

Design novel reinforcement learning algorithms for humanoid robot locomotion that achieve robust sim-to-real transfer. You will implement custom algorithm components in the PPO (Proximal Policy Optimization) framework that enable policies to follow diverse velocity commands with natural, stable gaits.

Research Question

What algorithm implementations (network architecture, policy optimization, experience replay) lead to policies that can successfully execute diverse locomotion commands in sim2sim transfer (Isaac Gym → MuJoCo)?

The key challenge: standard PPO implementations often struggle with diverse command following and sim-to-real transfer. Your algorithm modifications should improve:

• Policy robustness across varied commands (different speeds, directions, turning rates)
• Sample efficiency during training
• Generalization from simulation to simulation (Isaac Gym → MuJoCo)
• Natural, energy-efficient gaits
• Stable transitions between different commands

Background

The humanoid locomotion task requires the robot to track 3D velocity commands:

• vx: forward/backward velocity (m/s)
• vy: lateral velocity (m/s)
• dyaw: yaw angular velocity (rad/s)

Standard PPO algorithm consists of three main components:

1. Actor-Critic Network (actor_critic.py): Neural network architecture with separate actor (policy) and critic (value function) heads
2. PPO Optimizer (ppo.py): Policy optimization using clipped surrogate objective, value function loss, and entropy regularization
3. Rollout Storage (rollout_storage.py): Experience buffer for collecting and processing trajectories

The problem: Standard implementations often:

• Struggle with diverse command distributions
• Have poor sample efficiency on complex locomotion tasks
• Fail to transfer between simulators (Isaac Gym → MuJoCo)
• Produce unnatural or unstable gaits
• Require extensive hyperparameter tuning

Task

Implement custom algorithm components in the EDITABLE SECTIONS:

1. Actor-Critic Network: actor_critic_custom.py (lines 36-128)

In ActorCritic.__init__ method:

• Design custom network architecture (layer sizes, activation functions)
• Add normalization layers (LayerNorm, BatchNorm, custom)
• Implement custom initialization schemes
• Add auxiliary heads or features

In ActorCritic.act method:

• Modify action sampling strategy
• Add custom exploration mechanisms
• Implement action post-processing

In ActorCritic.evaluate_actions method:

• Customize value function computation
• Modify action log probability calculation
• Add auxiliary losses or regularization

2. PPO Optimizer: ppo_custom.py (lines 39-185)

In PPO.__init__ method:

• Configure optimizer settings
• Set up learning rate schedules
• Initialize custom training components

In PPO.update method:

• Modify policy loss computation (clipping strategy, advantage normalization)
• Customize value function loss (Huber loss, clipping, multi-step returns)
• Adjust entropy regularization
• Implement custom gradient clipping or normalization
• Add auxiliary losses (e.g., behavioral cloning, imitation)

3. Rollout Storage: rollout_storage_custom.py (lines 32-182)

In RolloutStorage.__init__ method:

• Design custom buffer structure
• Add additional tracking tensors

In RolloutStorage.add_transitions method:

• Customize how experiences are stored
• Add data augmentation or preprocessing

In RolloutStorage.compute_returns method:

• Modify advantage estimation (GAE parameters, normalization)
• Implement custom return computation (n-step, λ-returns)
• Add reward shaping or preprocessing

Fixed components:

• Environment and reward functions
• Training: 4096 parallel environments, 10000 iterations
• Observation/action spaces

Evaluation

Trained in Isaac Gym (4096 parallel envs, 10000 iterations), then evaluated on 100 diverse random commands in MuJoCo (sim2sim transfer):

Test procedure:

1. Sample 100 random commands from ranges:
   • vx: [-0.5, 1.0] m/s
   • vy: [-0.4, 0.4] m/s
   • dyaw: [-0.5, 0.5] rad/s
2. For each command, run 10-second episode in MuJoCo
3. Success criteria:
   • Robot doesn't fall (base height > 0.3m, tilt < 0.5 rad)
   • Velocity tracking error < 0.3 (combined linear + angular)
   • Stable for at least 80% of steps

Metrics:

• success_rate: Percentage of commands successfully executed (primary metric)
• avg_vel_error: Average velocity tracking error across all trials
• fall_rate: Percentage of trials where robot fell

Success criteria: Good algorithms should achieve:

• High success rate (>70%) across diverse commands
• Low tracking error (<0.3 combined)
• Low fall rate (<20%)
• Robust sim2sim transfer (Isaac Gym → MuJoCo)

Reference Implementation

One baseline is provided:

default: Standard PPO implementation from humanoid-gym

• 3-layer MLP with [512, 256, 128] hidden units
• Standard PPO loss with clipping (ε=0.2)
• GAE with λ=0.95, γ=0.99
• Baseline for comparison

Hints

• Network architecture: Deeper networks, normalization layers, or residual connections may improve learning
• Advantage normalization: Proper normalization can stabilize training
• Value function: Accurate value estimates improve policy learning
• Exploration: Entropy regularization or noise injection can help exploration
• Sample efficiency: Better advantage estimation or multi-step returns can improve efficiency
• Sim2sim transfer: Algorithms that learn robust features transfer better
• Consider:
  • Adaptive learning rates or schedules
  • Custom loss weightings (policy vs value vs entropy)
  • Gradient clipping strategies
  • Observation/action normalization
  • Auxiliary tasks or losses