SWITCH: Benchmarking Modeling and Handling of Tangible Interfaces in Long-horizon Embodied Scenarios
arXiv:2511.17649v4 Announce Type: replace-cross Abstract: Tangible control interfaces (TCIs), such as appliance panels, remotes, elevators, and embedded GUIs, are a fundamental component of everyday human-built environments. Interacting with these interfaces requires agents not only to ground language in visual observations,but also to execute actions, track temporally evolving state changes, and verify whether intended outcomes have been achieved. However, existing benchmarks predominantly evaluate open-loop perception or single-step action execution, failing to capture this continuous cycle
The proliferation of complex physical environments necessitates more robust AI interaction benchmarks, pushing research toward real-world scenarios beyond perception or single-step actions.
Improved benchmarks for tangible interfaces are critical for developing truly autonomous AI agents and humanoid robots capable of operating effectively in human-centric environments.
The focus is shifting from isolated AI tasks to integrated, long-horizon interactions, demanding continuous state tracking, action execution, and outcome verification in physical settings.
- · AI agents developers
- · Robotics companies
- · Smart appliance manufacturers
- · AI companies focused solely on perception
- · Developers of limited-scope AI benchmarks
Research in embodied AI and robotics will accelerate due to a standardized method for evaluating complex interactions.
More practical and adaptable AI systems will emerge for a wider range of physical tasks, including household chores and industrial operations.
The commercial viability and adoption rate of general-purpose humanoid robots capable of sophisticated interaction will increase significantly.
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Read at arXiv cs.AI