arXiv:2606.27268v1 Announce Type: cross Abstract: Recently, a few works have made early attempts to study test-time scaling for embodied tasks. However, two major challenges remain unsolved: (1) reasoning can effectively improve the performance of the policy, but its scaling mechanism has seldom been studied; (2) historical information is essential, as embodied tasks are inherently long-horizon and sequential, making sole reliance on current observations for action scaling inadequate due to the lack of historical context utilization. To address these challenges, we introduce E-TTS, a modular a
This research addresses fundamental limitations in current embodied AI, particularly regarding test-time scaling and leveraging historical context, which are critical for advancing robotic manipulation capabilities.
Improving robotic manipulation through better reasoning and historical data utilization is essential for progressing beyond controlled environments towards general-purpose, robust autonomous systems, impacting industries from logistics to personal assistance.
The E-TTS framework introduces a new modular approach to test-time scaling for embodied tasks, promising more robust and effective robotic policies by addressing challenges in reasoning and historical information utilization.
- · Robotics companies
- · AI research institutions
- · Logistics and manufacturing sectors
- · Companies relying on manual labor for complex manipulation tasks (in the long te
More capable and adaptable robotic systems will emerge for complex real-world tasks.
The cost of deploying robots in dynamic, unstructured environments could decrease significantly, expanding their applications.
This could accelerate the development of humanoid robots capable of general tasks, blurring the lines between human and machine labor.
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Read at arXiv cs.AI