ADAPT augments planners with affordance reasoning to raise task success in environments with unspecified and time-varying object affordances, and a LoRA-finetuned VLM backend beats GPT-4o on the new DynAfford benchmark.
Hierarchical task learning from language instructions with unified transformers and self- monitoring
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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PaLM-E is a single 562B-parameter multimodal model that performs embodied reasoning tasks like robotic manipulation planning and visual question answering by interleaving vision, state, and text inputs with positive transfer from joint training on language and robotics data.
EUEA fine-tunes VLMs on object perception, task planning, action understanding and goal recognition, with recovery and GRPO, to raise ALFRED success rates by 11.89% over behavior cloning.
citing papers explorer
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ADAPT: Benchmarking Commonsense Planning under Unspecified Affordance Constraints
ADAPT augments planners with affordance reasoning to raise task success in environments with unspecified and time-varying object affordances, and a LoRA-finetuned VLM backend beats GPT-4o on the new DynAfford benchmark.
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PaLM-E: An Embodied Multimodal Language Model
PaLM-E is a single 562B-parameter multimodal model that performs embodied reasoning tasks like robotic manipulation planning and visual question answering by interleaving vision, state, and text inputs with positive transfer from joint training on language and robotics data.
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Environmental Understanding Vision-Language Model for Embodied Agent
EUEA fine-tunes VLMs on object perception, task planning, action understanding and goal recognition, with recovery and GRPO, to raise ALFRED success rates by 11.89% over behavior cloning.