ATOM-Bench: A Real-World Benchmark for Atomic Skills and Compositional Generalization in Manipulation Policies
arXiv:2606.16826v1 Announce Type: cross Abstract: Generalist manipulation policies are increasingly presented as foundation models for robotic control, but their real-world generalization remains difficult to diagnose. A policy may succeed on demonstrated tasks while still failing to execute fine-grained atomic skills or recombine learned skills in new task structures. We introduce \textbf{ATOM-Bench}, a real-world benchmark for evaluating both atomic skills and compositional generalization in manipulation policies. ATOM-Bench factorizes tabletop manipulation into motor atoms and instruction a
The increasing development of generalist manipulation policies and foundation models for robotic control necessitates robust, real-world benchmarks to diagnose their effectiveness and limitations.
A robust benchmark for robotic skills will accelerate the development and deployment of more capable and reliable AI-driven robotic systems, impacting various industries.
The introduction of ATOM-Bench provides a standardized, real-world method for evaluating robotic manipulation generalisation, moving beyond simulated or narrow task successes.
- · Robotics researchers
- · AI hardware manufacturers
- · Automation industry
- · Manufacturers using robotics
- · Companies with proprietary, non-standardized robot testing methods
Improved real-world performance and reliability of generalist manipulation robots.
Faster adoption of robots in complex, unstructured environments previously inaccessible to automation.
Enhanced AI agents embedded in physical robotic forms, leading to more versatile and autonomous systems.
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Read at arXiv cs.AI