  Recent advancements in robotics have enabled robots to navigate complex
scenes or manipulate diverse objects independently. However, robots are still
impotent in many household tasks requiring coordinated behaviors such as
opening doors. The factorization of navigation and manipulation, while
effective for some tasks, fails in scenarios requiring coordinated actions. To
address this challenge, we introduce, HarmonicMM, an end-to-end learning method
that optimizes both navigation and manipulation, showing notable improvement
over existing techniques in everyday tasks. This approach is validated in
simulated and real-world environments and adapts to novel unseen settings
without additional tuning. Our contributions include a new benchmark for mobile
manipulation and the successful deployment in a real unseen apartment,
demonstrating the potential for practical indoor robot deployment in daily
life. More results are on our project site:
https://rchalyang.github.io/HarmonicMM/
