arXiv Computer Vision
Learning to Live: A Step Toward Lifelong Embodied Intelligence
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arXiv Machine Learning
4d ago
When the Past Shapes Action: Understanding Embodied Memory
The paper "When the Past Shapes Action: Understanding Embodied Memory" surveys how embodied AI agents can preserve and use past observations, actions, and experiences to improve decision-making in dynamic environments. It categorizes existing approaches along six memory requirements—world-state persistence, task progress, belief maintenance, experiential learning, etc.—and eight architectural styles such as recurrent states, fast weights, and spatial memory. The authors evaluate how current benchmarks test memory-dependent behavior and highlight open challenges in selecting relevant experiences, maintaining consistent beliefs, and consolidating knowledge across episodes.
arXiv AI
Jul 17
MEMORA: Embodied Action Memory from Egocentric Videos for Reasoning and Planning
arXiv:2607. 14252v1 Announce Type: cross Abstract: Long-horizon robot planning requires more than predicting what actions will do next; it also requires memory of the embodied experience that makes future goals interpretable.
arXiv Computer Vision
Oct 27
ReSight: Task-Driven Self-Improving Memory for Embodied Agents
arXiv:2610.33487v1 Announce Type: cross Abstract: Robots can use historical observations to answer spatiotemporal queries in familiar environments, but the presence of relevant evidence does not impl...
arXiv AI
Jul 7
HiMe: Hierarchical Embodied Memory for Long-Horizon Vision-Language-Action Control
arXiv:2607. 03449v1 Announce Type: cross Abstract: Current Vision-Language-Action (VLA) models excel at robotic manipulation but often struggle with non-Markovian tasks requiring long-term memory and reasoning due to their reliance on immediate observations.
Hugging Face Trending Papers
Oct 19
BetaMem: Learning to Remember a Changing World for Embodied Agents
Long-horizon embodied agents face temporal confusion and incomplete or distorted records, recurring errors from fixed memory rules, and growing storage and retrieval costs. We present BetaMem, a frame...
arXiv AI
Aug 19
Teach and Grow: An Agent-Centered Architecture for General Robot Learning
Teach-and-Grow Learning (TGL) is an agent-centered architecture that transforms a few successful demonstrations into reusable Skill Blocks, enabling a robot to compose, execute, and revise behaviors in new scenes without task-specific policy retraining. The system maintains a Skill Library and structured Experience Memory to capture successes, failures, and repairs, allowing persistent reuse and agent-directed adaptation. Evaluation on the LIBERO benchmark shows state-of-the-art performance, and the authors propose a scaling-law hypothesis suggesting that accumulated reusable experience reduces future-task error and teaching demand following a power-law trend.