A framework merges diffusion-based motion priors with force-feedback MPC to enable reliable tool insertion, force tracking, and collision-free circular motions in robotic deburring.
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Pith papers citing it
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cs.RO 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
MOMO integrates kinesthetic teaching, a tool-based LLM for safe language adaptation, Kernelized Movement Primitives, probabilistic virtual fixtures, and ergodic control to support seamless physical, verbal, and graphical robot skill learning and adaptation.
citing papers explorer
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Learning-Guided Force-Feedback Model Predictive Control with Obstacle Avoidance for Robotic Deburring
A framework merges diffusion-based motion priors with force-feedback MPC to enable reliable tool insertion, force tracking, and collision-free circular motions in robotic deburring.
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MOMO: A framework for seamless physical, verbal, and graphical robot skill learning and adaptation
MOMO integrates kinesthetic teaching, a tool-based LLM for safe language adaptation, Kernelized Movement Primitives, probabilistic virtual fixtures, and ergodic control to support seamless physical, verbal, and graphical robot skill learning and adaptation.