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ARXIV:2603.24018 · EMBODIED AGENTS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.24018EMBODIED AGENTSSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEBingqing Wei · Zhongyu Xia · Dingai Liu · Xiaoyu Zhou · Zhiwei Lin · Yongtao Wang · arXiv
An embodied agent framework that continuously learns from environment interaction to improve task execution and generalize to new tasks.
Opportunity summary
Pain An embodied agent framework that continuously learns from environment interaction to improve task execution and generalize to new tasks.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
An embodied agent framework that continuously learns from environment interaction to improve task execution and generalize to new tasks. These failures arise from a fundamental gap between the static training data of VLMs and…
Vision-language models (VLMs) have shown remarkable general capabilities, yet embodied agents built on them fail at complex tasks, often skipping critical steps, proposing invalid actions, and repeating mistakes. These failures arise from a fundamental…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. To address this issue, we introduce ELITE, an embodied agent framework with {E}xperiential {L}earning and {I}ntent-aware {T}ransfer that enables agents to continuously learn from…
Embodied Agents moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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An embodied agent framework that continuously learns from environment interaction to improve task execution and generalize to new tasks.
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10.48550/arXiv.2603.24018An embodied agent framework that continuously learns from environment interaction to improve task execution and generalize to new tasks.
Abstract
Vision-language models (VLMs) have shown remarkable general capabilities, yet embodied agents built on them fail at complex tasks, often skipping critical steps, proposing invalid actions, and repeating mistakes. These failures arise from a fundamental gap between the static training data of VLMs and the physical interaction for embodied tasks. VLMs can learn rich semantic knowledge from static data but lack the ability to interact with the world. To address this issue, we introduce ELITE, an embodied agent framework with {E}xperiential {L}earning and {I}ntent-aware {T}ransfer that enables agents to continuously learn from their own environment interaction experiences, and transfer acquired knowledge to procedurally similar tasks. ELITE operates through two synergistic mechanisms, \textit{i.e.,} self-reflective knowledge construction and intent-aware retrieval. Specifically, self-reflective knowledge construction extracts reusable strategies from execution trajectories and maintains an evolving strategy pool through structured refinement operations. Then, intent-aware retrieval identifies relevant strategies from the pool and applies them to current tasks. Experiments on the EB-ALFRED and EB-Habitat benchmarks show that ELITE achieves 9\% and 5\% performance improvement over base VLMs in the online setting without any supervision. In the supervised setting, ELITE generalizes effectively to unseen task categories, achieving better performance compared to state-of-the-art training-based methods. These results demonstrate the effectiveness of ELITE for bridging the gap between semantic understanding and reliable action execution.
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What was readable
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Viability
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Commercial
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Dimensions overall score 7.0
PROBLEM
An embodied agent framework that continuously learns from environment interaction to improve task execution and generalize to new tasks. These failures arise from a fundamental gap between the static training data of VLMs and the physical interaction for embodied tasks.
METHOD
Vision-language models (VLMs) have shown remarkable general capabilities, yet embodied agents built on them fail at complex tasks, often skipping critical steps, proposing invalid actions, and repeating mistakes. These failures arise from a fundamental gap between the static tra...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. To address this issue, we introduce ELITE, an embodied agent framework with {E}xperiential {L}earning and {I}ntent-aware {T}ransfer that enables agents to continuously learn from their own environment int...
WHY NOW
Embodied Agents moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
An embodied agent framework that continuously learns from environment interaction to improve task execution and generalize to new tasks. These failures arise from a fundamental gap between the static training data of VLMs and the physical interaction for embodied tasks.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Vision-language models (VLMs) have shown remarkable general capabilities, yet embodied agents built on them fail at complex tasks, often skipping critical steps, proposing invalid actions, and repeating mistakes. These failures arise from a fundamental gap between the static training data of VLMs and the physical interaction for embodied tasks.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. To address this issue, we introduce ELITE, an embodied agent framework with {E}xperiential {L}earning and {I}ntent-aware {T}ransfer that enables agents to continuously learn from their own environment interaction experiences, and transfer acquired knowledge to procedurally similar tasks. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Embodied Agents moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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An embodied agent framework that continuously learns from environment interaction to improve task execution and generalize to new tasks.
Segment
Embodied Agents
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Commercial read
7.0/10 public viability
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