Humanoid robots show promise for complex whole-body tasks in unstructured environments. Although Human-Object Interaction (HOI) has advanced, most methods focus on fully actuated objects rigidly coupled to the robot, ignoring underactuated objects with independent dynamics and non-holonomic constraints. These introduce control challenges from coupling forces and occlusions. We present HAIC, a unified framework for robust interaction across diverse object dynamics without external state estimation. Our key contribution is a dynamics predictor that estimates high-order object states (velocity, acceleration) solely from proprioceptive history. These predictions are projected onto static geometric priors to form a spatially grounded dynamic occupancy map, enabling the policy to infer collision boundaries and contact affordances in blind spots. We use asymmetric fine-tuning, where a world model continuously adapts to the student policy's exploration, ensuring robust state estimation under distribution shifts. Experiments on a humanoid robot show HAIC achieves high success rates in agile tasks (skateboarding, cart pushing/pulling under various loads) by proactively compensating for inertial perturbations, and also masters multi-object long-horizon tasks like carrying a box across varied terrain by predicting the dynamics of multiple objects.

翻译：人形机器人在非结构化环境中展现出执行复杂全身任务的潜力。尽管人-物交互研究已取得进展，但现有方法多聚焦于与机器人刚性连接的全驱动物体，忽略了具有独立动力学特性及非完整约束的欠驱动物体。此类物体因耦合作用力与视觉遮挡问题带来了控制挑战。本文提出HAIC，一个无需外部状态估计即可适应多种物体动力学特性的鲁棒交互统一框架。我们的核心贡献在于设计了一个动态预测器，该预测器仅依赖本体感知历史数据即可估计物体的高阶状态（速度、加速度）。这些预测结果被映射到静态几何先验上，形成空间锚定的动态占据地图，使控制策略能够推断盲区中的碰撞边界与接触可供性。我们采用非对称微调方法，使世界模型持续适应学生策略的探索过程，确保在分布偏移下的鲁棒状态估计。在人形机器人上的实验表明：HAIC通过主动补偿惯性扰动，在敏捷任务（滑板运动、不同负载下的推/拉购物车）中实现了高成功率；同时通过预测多物体动力学，能够完成长时程多物体任务（如携带箱子穿越复杂地形）。