Small amount of human input can improve mobile robots in real work
Improving robots in real settings can be done with small amounts of human input and smart corrections.
- Publication
- arXiv
- Stage
- Preprint
- What we read
- Summary of the paper
- Authors
- Zhefei Gong, Hefeifei Jiang, Zehao Wang, Jiachen Li
- Universities and research institutions
- Not yet supplied in verified metadata; the Brief does not guess.
What they did and found
A test framework adds tiny corrective tweaks to an existing robot controller, split between moving base and operating a robotic arm, guided by limited human input. Real-world trials showed higher task success and less need for steady human control across several mobile‑manipulation tasks.
Why it matters
For shops or services using robots, this shows a practical way to tailor robots to tasks with modest time and effort, without costly rebuilds.
This approach keeps the base motion policy and adds small fixes linked to how the robot moves. It separates base movement from arm work to avoid destabilizing the system.
In practice, you may raise performance by a meaningful amount with weeks of real work and modest human input, without rebuilding the whole software stack.
What remains uncertain
Tests used one robot platform and few trials; results may not apply to different robots or tasks yet, in settings.
Original sources · 1
- Hierarchical Residual Policy Learning for Real-World Mobile Manipulation with Sparse Human Guidance ↗arXiv · 2026-10-14
Check the original paper for its authors, methods, version and access terms.
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