BARS-RL: Bimanual Articulated-object manipulation under Resistance with Stabilization

Robots in human environments frequently interact with articulated objects that rest unanchored on a surface (e.g., microwaves, toaster ovens), and opening these objects requires overcoming an initial opening resistance. A pulling force below this resistance leaves the moving part closed, whereas one above it can drag the object body, so one arm must pull while the other holds the object body in place. Existing methods that assume anchored objects or negligible opening resistance do not capture this coupling between opening and stabilization. To address these limitations, we propose BARS-RL, a reinforcement learning framework in which a single policy allocates force between a task arm and a support arm without observing the resistance or friction. A stabilization reward penalizes contact forces near the base-slip boundary, providing a training signal before the object body moves. Training follows a curriculum organized by the resistance-to-friction ratio, which moves from an anchored object body to an unanchored one and finally mixes episodes that base friction alone can hold with those that require support. On three simulated objects with revolute and prismatic joints, BARS-RL achieves an average success rate of 83.5%, outperforming baselines while approaching the performance of an oracle that observes the resistance and friction.