Derail adversarial perturbations hijack the scoring head in generative E2E driving planners, flipping safe to unsafe trajectory selection with 39-80% score drops and up to 50% collision rates.
Generative artificial intelligence in robotic manipulation: a survey
7 Pith papers cite this work. Polarity classification is still indexing.
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AgentChord models manipulation tasks as directed graphs enriched with anticipatory recovery branches, using specialized agents to enable immediate, low-latency failure responses and improve success on long-horizon bimanual tasks.
VLAMotor exposes VLA failures via distance-aware uncertainty testing and synthesizes agent-planned repair data to fine-tune models, reporting 49.25% success rate gains in simulation and 57.5% on hardware.
EmbodiedClaw automates embodied AI development workflows through conversation, reducing manual effort and improving consistency and reproducibility.
This survey organizes large VLM-based VLA models for robotic manipulation into monolithic and hierarchical paradigms, reviews their integrations and datasets, and outlines future directions.
A feed-forward Gaussian-splatting system reconstructs photo-realistic 3D scenes from single-view panoramas in seconds via cube-map decomposition and depth-aware fusion for robotic simulation use.
The paper surveys 3D generation techniques for embodied AI and robotics, categorizing them into data generation, simulation environments, and sim-to-real bridging while identifying bottlenecks in physical validity and transfer.
citing papers explorer
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Off the Rails: Hijacking the Scoring Head in Generative End-to-End Driving Planners with Safety-Violating Adversarial Perturbations
Derail adversarial perturbations hijack the scoring head in generative E2E driving planners, flipping safe to unsafe trajectory selection with 39-80% score drops and up to 50% collision rates.
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From Reaction to Anticipation: Proactive Failure Recovery through Agentic Task Graph for Robotic Manipulation
AgentChord models manipulation tasks as directed graphs enriched with anticipatory recovery branches, using specialized agents to enable immediate, low-latency failure responses and improve success on long-horizon bimanual tasks.
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VLAMotor: Test-Guided Enhancement of Vision-Language-Action Models via Agent-BasedData Synthesis
VLAMotor exposes VLA failures via distance-aware uncertainty testing and synthesizes agent-planned repair data to fine-tune models, reporting 49.25% success rate gains in simulation and 57.5% on hardware.
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EmbodiedClaw: Conversational Workflow Execution for Embodied AI Development
EmbodiedClaw automates embodied AI development workflows through conversation, reducing manual effort and improving consistency and reproducibility.
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Large VLM-based Vision-Language-Action Models for Robotic Manipulation: A Survey
This survey organizes large VLM-based VLA models for robotic manipulation into monolithic and hierarchical paradigms, reviews their integrations and datasets, and outlines future directions.
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Genie Sim PanoRecon: Fast Immersive Scene Generation from Single-View Panorama
A feed-forward Gaussian-splatting system reconstructs photo-realistic 3D scenes from single-view panoramas in seconds via cube-map decomposition and depth-aware fusion for robotic simulation use.
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3D Generation for Embodied AI and Robotic Simulation: A Survey
The paper surveys 3D generation techniques for embodied AI and robotics, categorizing them into data generation, simulation environments, and sim-to-real bridging while identifying bottlenecks in physical validity and transfer.