Research map / Multi-agent systems and games / LLM agents
CrafterDojo: A Suite of Foundation Models for Building Open-Ended Embodied Agents in Crafter
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Introduces CrafterDojo, a suite of Crafter foundation models (C-VPT, C-CLIP, C-Steve-1) and data generators (CrafterPlay, CrafterCaption) to serve as a lightweight Minecraft-like testbed.
Problem
Crafter is a lightweight alternative to Minecraft for embodied agent research but lacks foundation models and large-scale behavior and caption datasets needed to develop them.
Approach
Automatically generate behavior demonstrations with a Craftax-trained PPO expert and captions with a rule-based generator, then port VPT, MineCLIP, and Steve-1 recipes to Crafter, adding event-based packed hindsight relabeling and a CVAE text-to-goal prior.
Key results
- C-VPT (large) reaches 61.4% Crafter Score and C-VPT (base) 71.8% return, outperforming prior baselines (e.g., 31.8% score for the strongest prior method).
- C-CLIP reaches 89.8% R@1 on CrafterCaption held-out episodes versus 1.7% for CLIP4Clip.
- Instruction chaining with a heuristic planner raises C-Steve-1 success on T4 from 33% to 80%.
Surprising findings
- C-Steve-1 with a single instruction underperforms on multi-step tasks, motivating planning.
- Removing all noop actions hurts C-VPT, so selective filtering of short noop runs is needed.
Limitations
- Multi-step tasks are weak for single-instruction C-Steve-1 and rely on hand-designed or PPO planners.
- Long-horizon evaluations use a simplified Crafter variant without health decay and mobs for T1.
- Caption and data generation rely on rule-based templates with limited linguistic diversity before paraphrasing.
Experimental setup
Datasets: CrafterPlay CrafterCaption
Benchmarks: Crafter
Metrics: Crafter Score Return Recall@1 Recall@5 Recall@10 Mean Rank success rate
Baselines: Δ-IRIS CLIP4Clip PPO-RNN C-VPT-RL Single Instruction C-Steve-1
Builds on: VPT MineCLIP Steve-1 Craftax LaCLIP
Closest papers by meaning
- ADAM: An Embodied Causal Agent in Open-World Environments (ICLR 2025 · similarity 0.88)
- MP5: A Multi-modal Open-ended Embodied System in Minecraft via Active Perception (CVPR 2024 · similarity 0.88)
- LARM: Large Auto-Regressive Model for Long-Horizon Embodied Intelligence (ICML 2025 · similarity 0.87)
- MindForge: Empowering Embodied Agents with Theory of Mind for Lifelong Cultural Learning (NeurIPS 2025 · similarity 0.87)
- Embodied-R1.5: Evolving Physical Intelligence via Embodied Foundation Models (NeurIPS 2026 · similarity 0.87)
- SimWorld Studio: Automatic Environment Generation with Evolving Coding Agent for Embodied Agent Learning (NeurIPS 2026 · similarity 0.87)
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