  Interactions with articulated objects are a challenging but important task
for mobile robots. To tackle this challenge, we propose a novel closed-loop
control pipeline, which integrates manipulation priors from affordance
estimation with sampling-based whole-body control. We introduce the concept of
agent-aware affordances which fully reflect the agent's capabilities and
embodiment and we show that they outperform their state-of-the-art counterparts
which are only conditioned on the end-effector geometry. Additionally,
closed-loop affordance inference is found to allow the agent to divide a task
into multiple non-continuous motions and recover from failure and unexpected
states. Finally, the pipeline is able to perform long-horizon mobile
manipulation tasks, i.e. opening and closing an oven, in the real world with
high success rates (opening: 71%, closing: 72%).
