Predictive Auxiliary Objective for Payload-Adaptive Multi-Motion Humanoid Balancing

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Achieving versatile humanoid control requires a single policy that can execute many diverse motions while remaining robust to unobserved environment conditions, such as payloads carried in the robot's hands. Each of these requirements is demanding on its own, and combining them compounds the difficulty: the same policy must represent motions with very different dynamics and, for each of them, infer conditions it never observes directly. A common way to achieve such robustness in reinforcement learning is to condition the policy on a long history of observations, encoded by a history encoder trained end-to-end. We show that this history encoder can be trained more effectively with an auxiliary future-observation prediction objective, derived from intention-conditioned flow occupancy models, which treats each motion-payload combination as a latent \textit{intention} to be inferred from history. The prediction objective is optimized online from a replay buffer of recent trajectories, alongside standard PPO. We evaluate on a challenging benchmark of dynamic whole-body balancing motions with unknown hand payloads on a full-size Unitree H1-2. Our method improves normalized return over A-RMA and long-short-history baselines in simulation, and increases single-leg balancing time under load on hardware. Videos and code are available at https://macstepien.github.io/PAO/