We lifted 7 functions out of this paper's own repositories and ran 6 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| xyz9911/FLAME | canonical | 6 of 7 |
| Function | Status | Where it lives |
|---|---|---|
| repeat_kv | Ran | xyz9911/FLAME/llm_nav/model/llama_xformer.py code served (permissive licence) · get_code("30d7eec482ebf6b1") |
| apply_rotary_pos_emb | Ran | xyz9911/FLAME/llm_nav/model/llama_xformer.py code served (permissive licence) · get_code("f725bc2d76076485") |
| exists | Ran | xyz9911/FLAME/llm_nav/model/modeling_flamingo.py code served (permissive licence) · get_code("aa5486a3650902d8") |
| getattr_recursive | Ran | xyz9911/FLAME/llm_nav/model/modeling_flamingo.py code served (permissive licence) · get_code("fe68fff801021b23") |
| inference | Ran | xyz9911/FLAME/llm_nav/agent.py code served (permissive licence) · get_code("37b19b055de49001") |
| rotate_half | Ran | xyz9911/FLAME/llm_nav/model/llama_xformer.py code served (permissive licence) · get_code("b99eea6376d1e212") |
| unwrap_model | Not yet run | xyz9911/FLAME/llm_nav/trainer.py code served (permissive licence) · get_code("54b2d55aa476a3bc") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Large Language Models (LLMs) have demonstrated potential in Vision-and-Language Navigation (VLN) tasks, yet current applications face challenges. While LLMs excel in general conversation scenarios, they struggle with specialized navigation tasks, yielding suboptimal performance compared to specialized VLN models. We introduce FLAME (FLAMingo-Architected Embodied Agent), a novel Multimodal LLM-based agent and architecture designed for urban VLN tasks that efficiently handles multiple observations. Our approach implements a three-phase tuning technique for effective adaptation to navigation tasks, including single perception tuning for street view description, multiple perception tuning for route summarization, and end-to-end training on VLN datasets. The augmented datasets are synthesized automatically. Experimental results demonstrate FLAME's superiority over existing methods, surpassing state-of-the-art methods by a 7.3% increase in task completion on Touchdown dataset. This work showcases the potential of Multimodal LLMs (MLLMs) in complex navigation tasks, representing an advancement towards applications of MLLMs in the field of embodied intelligence.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2408.11051")
get_code_for_paper("2408.11051")
have("2408.11051")
Connect an agent — have() is free.