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A Survey on Robotics with Foundation Models: toward Embodied AI
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While the exploration for embodied AI has spanned multiple decades, it remains a persistent challenge to endow agents with human-level intelligence, including perception, learning, reasoning, decision-making, control, and generalization capabilities, so that they can perform general-purpose tasks in open, unstructured, and dynamic environments. Recent advances in computer vision, natural language processing, and multi-modality learning have shown that the foundation models have superhuman capabilities for specific tasks. They not only provide a solid cornerstone for integrating basic modules into embodied AI systems but also shed light on how to scale up robot learning from a methodological perspective. This survey aims to provide a comprehensive and up-to-date overview of foundation models in robotics, focusing on autonomous manipulation and encompassing high-level planning and low-level control. Moreover, we showcase their commonly used datasets, simulators, and benchmarks. Importantly, we emphasize the critical challenges intrinsic to this field and delineate potential avenues for future research, contributing to advancing the frontier of academic and industrial discourse.
Forward citations
Cited by 12 Pith papers
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VLM-driven embodied agents complete household tasks but routinely violate spatial-relation safety preconditions, according to the new 507-sample SafeRelBench evaluation.
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ACE-Brain-0.5: A Unified Embodied Foundational Model for Physical Agentic AI
A single 8B backbone unifies spatial perception, decision making, navigation/manipulation, and progress estimation with SSR+ merging, reporting gains on most spatial benchmarks and competitive action/progress results.
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Self-Evolving Just-In-Time Memory for Proactive Embodied Safety
A Just-In-Time Memory framework with graph-based state tracking and self-evolving safety skills improves safe task completion in household robots by up to 30 percentage points on IS-Bench.
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From Video to Control: A Survey of Learning Manipulation Interfaces from Temporal Visual Data
Video-to-robot control methods cluster into three interface families, and the field’s main bottleneck is grounding video-derived predictions into dependable closed-loop robot behavior.
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ANNIE: Be Careful of Your Robots
The authors build a safety-centered benchmark and attack method that induces vision-language-action robot policies to violate ISO-based safety rules in a majority of tested episodes.
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Disrupting Vision-Language Model-Driven Navigation Services via Adversarial Object Fusion
AdvOF crafts 3D adversarial objects that mislead VLM perception across multiple views and degrade VLN agent navigation success in simulation.
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Reinforced Reasoning for Embodied Planning
An SFT-plus-GRPO recipe lifts a 7B VLM to 35.6 percent success on EB-ALFRED versus 22.0 for GPT-4o-mini and 33.7 for Qwen2.5-VL-72B, with smaller but consistent gains on unseen EB-Habitat.
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RoboGPT-R1: Enhancing Robot Task Planning with Reinforcement Learning
A 3B VLM trained with SFT plus GRPO and an LCS-based reward reaches 55.3% on EmbodiedBench's EB-ALFRED, beating GPT-4o-mini and the 7B REBP planner.
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FlashDP: Private Training Large Language Models with Efficient DP-SGD
FlashDP fuses per-sample gradient computation, norm calculation, clipping, and noise addition into a cache-friendly block-wise all-reduce workflow that avoids explicit per-sample gradient storage and redundant recomputation.
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Towards Dual-Brain Minimal Sufficient Representation for Vision-Language Navigation
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Foundation Models in Robotics: A Comprehensive Review of Methods, Models, Datasets, Challenges and Future Research Directions
Foundation models are enabling a shift in robotics toward general-purpose agents, and this review maps the methods, models, datasets, challenges, and future directions across the field.
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Large Model Empowered Embodied AI: A Survey on Decision-Making and Embodied Learning
A review that categorizes large-model-empowered embodied AI into hierarchical and end-to-end decision-making, imitation and reinforcement learning, and world models.
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