arXiv:2510.14828v3 Announce Type: replace Abstract: Improving the reasoning capabilities of embodied agents is crucial for robots to complete complex human instructions in long-view manipulation tasks successfully. Despite the success of large language models and vision language models based on Supervised Fine-Tuning (SFT) in planning tasks, they continue facing challenges in performing long-horizon manipulation tasks in complex real-world environments, owing to their restricted common sense and reasoning capabilities. Considering that aligning general-purpose vision language models to robotic
The continuous advancements in AI, particularly Large Language Models (LLMs) and Vision Language Models (VLMs), are pushing the boundaries of robot reasoning, leading to new research focusing on enhancing their capabilities for complex, long-horizon tasks.
Improving robot reasoning and task planning through reinforcement learning is crucial for developing robust, general-purpose robots capable of operating autonomously in unstructured real-world environments, accelerating their commercial viability.
This research suggests a path towards more intelligent and adaptable embodied agents, moving beyond the limitations of SFT-based models and enabling robots to better handle complex human instructions and real-world variability.
- · Robotics companies
- · AI research institutions
- · Automation sector
- · Logistics and manufacturing
- · Manual labor in repetitive tasks
- · Companies relying on basic automation
Robots will become more proficient in understanding and executing complex instructions in dynamic environments.
This improved capability will accelerate the deployment of autonomous robots in various industries, including healthcare, manufacturing, and consumer services.
The enhanced reasoning capabilities of robots could lead to widespread adoption of humanoids and other embodied AI, transforming labor markets and industrial processes globally.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI