ActiveArena: Benchmarking and Understanding Active Perception in Robotic Manipulation
Quick summary
arXiv:2609.24124v1 Announce Type: cross Abstract: Active perception and manipulation are crucial for robots to interact with complex scenes. Existing benchmarks struggle to evaluate how robots effectively acquire and maintain information in memory in an active manner. To this end, we introduce ActiveArena-Sim, an active-perception simulator with controllable viewpoints and large-scale workspaces as the foundation. Built on this, we propose ActiveArena-Bench, which comprises 35 tasks across 5 fine-grained categories, covering visual exploration and interactive information acquisition. Each task
Key takeaways
- arXiv:2609.24124v1 Announce Type: cross Abstract: Active perception and manipulation are crucial for robots to interact with complex scenes.
- Existing benchmarks struggle to evaluate how robots effectively acquire and maintain information in memory in an active manner.
- To this end, we introduce ActiveArena-Sim, an active-perception simulator with controllable viewpoints and large-scale workspaces as the foundation.
Why it matters
This is more than a company headline: it shows who controls infrastructure, users and data in the AI value chain. The practical effect will appear in product integration, pricing and delivered capacity.
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