它展示了一个简洁的单阶段训练流程，通过从本体感觉历史中学习潜在上下文，替代了特权地形和速度输入，在真实硬件约束下实现了接近特权信息的运动能力。
A reproduction of the two core contributions of DreamWaQ (Nahrendra, Yu & Myung, ICRA 2023) in NVIDIA Isaac Lab on a Unitree Go2: an asymmetric actor-critic and CENet, a context-aided estimator that lets the deployed policy run on proprioception alone.
Result: the proprioception-only policy matched a privileged baseline on terrain difficulty and episode length, trailing it by ~3 reward and ~0.1 m/s tracking error. CENet recovered true body velocity to MSE ≈ 0.006 (m/s)² from proprioceptive history alone.
https://github.com/user-attachments/assets/daabeb17-dc1e-4bb3-8c0d-82a0601c9b6a
Trained Go2 robots crossing discrete-obstacle terrain in Isaac Sim, each tracking its own velocity command.
In simulation an RL policy can see privileged information - the exact terrain under the robot, its true base velocity. A real quadruped has neither. It has joint encoders and an IMU. So a policy trained on privileged inputs cannot be deployed.
DreamWaQ's answer is to have the robot infer that hidden state from its own recent proprioceptive history, and to train the policy, the value function, and the estimator together in a single phase - no teacher-student distillation.
Asymmetric actor-critic. The actor sees only what hardware can measure (45-D proprioception) plus CENet's outputs. The critic, used only during training, additionally sees a 187-D terrain height scan and the true base velocity.
| Group | Dim | Contents | Seen by |
|---|---|---|---|
policy | 45 | ang. velocity, gravity, command, joint pos/vel, last action | actor + critic |
height_scan | 187 | 17×11 terrain height grid (privileged) | critic only |
privileged_vel | 3 | true base linear velocity | critic + CENet target |
Actor input 64 (45 + 3 velocity + 16 latent) · Critic input 235 (45 + 187 + 3)
CENet. A shared encoder over the last 5 proprioceptive frames (225-D) splits into two heads: a velocity head regressing 3-D body velocity, and a β-VAE head producing a 16-D latent context. Trained with a hybrid loss:
L = MSE(v̂, v) + MSE(ô, o) + β · KL( z ‖ N(0, I) )
CENet's outputs are detached before reaching the actor, so the policy's gradient cannot reshape the estimator - it learns only from its own prediction error.
Both policies: 2000 iterations, se
Reproduction of DreamWaQ (ICRA 2023) in NVIDIA Isaac Lab - asymmetric actor-critic + CENet for proprioception-only quadrupedal locomotion on a Unitree Go2
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