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'''Systemic robustness and real-world brittleness'''
Autonomous robots remain highly vulnerable to unexpected changes in real-world environments. Even minor variations like a sudden beam of sunlight disrupting vision systems or unanticipated terrain irregularities can cause entire systems to fail<ref>{{Cite
'''Open-world scene understanding'''
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'''Multi-robot coordination and decentralization'''
Scaling robot systems raises thorny issues in coordination, safety, and communication. In '''multi-agent navigation''', challenges like deadlocks, selfish behaviors, and sample inefficiencies emerge. Innovations such as dividing planning into sub-problems, combining RL with imitation learning, hybrid centralized-decentralized approaches (e.g., prioritized communication learning), attention mechanisms, and graph transformers have shown promise, but large-scale, stable, real-time coordination remains an open frontier <ref>{{Cite journal |
'''Simulation-to-real (“reality gap”) transfer'''
[[Deep reinforcement learning]] is a powerful tool for teaching robots navigation and control, but training in simulation introduces discrepancies when deployed in reality. The '''reality gap''' (or differences between simulated and real environments) continues to impede reliable deployment, despite strategies to mitigate it<ref>{{Cite journal |
'''Hardware and bio''' '''hybrid constraints'''
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'''Ethics, liability, and societal integration'''
As robots become more autonomous, especially in public or collaborative roles, ethical and legal issues grow. Who is responsible when an autonomous system causes harm? Regulatory frameworks are still evolving to address liability, transparency, bias, and safety in systems like [[Self-driving car]] or socially interactive robots<ref>{{Cite
'''Embodied AI and industrial adoption'''
While AI algorithms have made strides, embedding them into robots (embodied AI) for real-world use remains slow-moving. Hardware constraints, economic viability, and infrastructure limitations limit widespread adoption. For instance, humanoid robots like “[[Pepper (robot)]]” failed to achieve ubiquity due to fundamental cost and complexity issues<ref>{{Cite web |title=Client Challenge |url=https://www.ft.com/content/84414ad5-6157-4f36-a27a-1366868a25ca |access-date=2025-08-25 |website=www.ft.com}}</ref><ref>{{Cite news |last=Hawkins |first=Amy |date=2025-04-21 |title=Humanoid workers and surveillance buggies:
==Societal impact and issues==
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