InterPrior: Scaling Generative Control for Physics-Based Human-Object Interactions

TL;DR

InterPrior is a scalable, unified generative framework that combines imitation learning and reinforcement learning to enable humanoid robots to perform diverse, physically coherent human-object interactions with improved generalization and adaptability.

Contribution

It introduces a goal-conditioned variational policy learned via imitation and reinforcement learning, capable of generalizing human-object interactions beyond training data.

Findings

Effective in generalizing to unseen objects and goals.

Enables user-interactive control of humanoid robots.

Potential for deployment in real robotic systems.

Abstract

Humans rarely plan whole-body interactions with objects at the level of explicit whole-body movements. High-level intentions, such as affordance, define the goal, while coordinated balance, contact, and manipulation can emerge naturally from underlying physical and motor priors. Scaling such priors is key to enabling humanoids to compose and generalize loco-manipulation skills across diverse contexts while maintaining physically coherent whole-body coordination. To this end, we introduce InterPrior, a scalable framework that learns a unified generative controller through large-scale imitation pretraining and post-training by reinforcement learning. InterPrior first distills a full-reference imitation expert into a versatile, goal-conditioned variational policy that reconstructs motion from multimodal observations and high-level intent. While the distilled policy reconstructs training behaviors, it does not generalize reliably due to the vast configuration space of large-scale human-object interactions. To address this, we apply data augmentation with physical perturbations, and then perform reinforcement learning finetuning to improve competence on unseen goals and initializations. Together, these steps consolidate the reconstructed latent skills into a valid manifold, yielding a motion prior that generalizes beyond the training data, e.g., it can incorporate new behaviors such as interactions with unseen objects. We further demonstrate its effectiveness for user-interactive control and its potential for real robot deployment.