CoRAL lets LLMs act as adaptive cost designers for motion planners while using VLM priors and online identification to handle unknown physics, achieving over 50% higher success rates than baselines in unseen contact-rich robotic scenarios.
Foundation models in robotics: Applications, challenges, and the future.The International Journal of Robotics Research, 44(5):701–739
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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.
RoboULM is a new human-in-the-loop LLM-based method for systematic uncertainty analysis in self-adaptive robots, backed by a dedicated taxonomy and positively evaluated by 16 practitioners across four industrial use cases.
R&B-EnCoRe uses self-supervised importance-weighted variational inference to distill action-predictive reasoning datasets that improve VLA performance on manipulation, navigation, and driving tasks without external verifiers.
EmbodiedClaw automates embodied AI development workflows through conversation, reducing manual effort and improving consistency and reproducibility.
citing papers explorer
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CoRAL: Contact-Rich Adaptive LLM-based Control for Robotic Manipulation
CoRAL lets LLMs act as adaptive cost designers for motion planners while using VLM priors and online identification to handle unknown physics, achieving over 50% higher success rates than baselines in unseen contact-rich robotic scenarios.
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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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Human-in-the-Loop Uncertainty Analysis in Self-Adaptive Robots Using LLMs
RoboULM is a new human-in-the-loop LLM-based method for systematic uncertainty analysis in self-adaptive robots, backed by a dedicated taxonomy and positively evaluated by 16 practitioners across four industrial use cases.
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Self-Supervised Bootstrapping of Action-Predictive Embodied Reasoning
R&B-EnCoRe uses self-supervised importance-weighted variational inference to distill action-predictive reasoning datasets that improve VLA performance on manipulation, navigation, and driving tasks without external verifiers.
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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.