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BISON system combines symbolic planning with imitation learning for AI agents

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new system called BISON that combines low-level imitation learning with high-level symbolic planning to tackle long-horizon tasks for embodied AI agents. This approach leverages neural policies for manipulation and control, alongside symbolic abstractions for efficient planning. Experiments on MetaWorld benchmarks show BISON's ability to generalize to problems with significantly more objects and longer horizons compared to existing methods, while also being more efficient in training and inference. AI

IMPACT Introduces a novel approach to long-horizon planning for embodied AI, potentially improving robotic task completion and generalization.

RANK_REASON Academic paper detailing a new system for AI planning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

BISON system combines symbolic planning with imitation learning for AI agents

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Academic paper detailing a new system for AI planning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Sheila A. McIlraith ·

    Learning Bilevel Policies over Symbolic World Models for Long-Horizon Planning

    We tackle the challenge of building embodied AI agents that can reliably solve long-horizon planning problems. Imitation learning from demonstrations has shown itself to be effective in training robots to solve a diversity of complex tasks requiring fine motor control and manipul…

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