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Donghun Noh

Publications and source records attributed to Donghun Noh.

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Yummy Operations Robot Initiative: Autonomous Cooking System Utilizing a Modular Robotic Kitchen and a Dual-Arm Proprioceptive Manipulator

This paper presents Yummy Operations Robot Initiative (YORI), a proprioceptive dual-arm robotic system that demonstrates autonomous multi-dish cooking for scalable food service applications. YORI integrates a dual-arm manipulator equipped with proprioceptive actuators, custom-designed tools, appliances, and a structured kitchen environment to address the complexities of cooking tasks. The proprioceptive actuators enable fast, precise, force-controlled movements while mitigating the risks associated with cooking-related impacts. The system's modular kitchen design and flexible tool-changing mechanism support simultaneous multi-dish preparation through torque control and optimization-based motion planning and scheduling. A comprehensive scheduling framework with dynamic rescheduling ensures reliable adaptation to new orders and delays. The system was publicly validated through live demonstrations, reliably preparing steak-frites across multiple convention sessions. This paper details YORI's design and explores future directions in kitchen optimization, task planning, and food quality control, demonstrating its potential as a scalable robotic cooking solution. A system introduction and cooking videos are available online.

cs.RO

Latent Adaptive Planner for Dynamic Manipulation

We present the Latent Adaptive Planner (LAP), a trajectory-level latent-variable policy for dynamic nonprehensile manipulation (e.g., box catching) that formulates planning as inference in a low-dimensional latent space and is learned effectively from human demonstration videos. During execution, LAP achieves real-time adaptation by maintaining a posterior over the latent plan and performing variational replanning as new observations arrive. To bridge the embodiment gap between humans and robots, we introduce a model-based proportional mapping that regenerates accurate kinematic-dynamic joint states and object positions from human demonstrations. Through challenging box catching experiments with varying object properties, LAP demonstrates superior success rates, trajectory smoothness, and energy efficiency by learning human-like compliant motions and adaptive behaviors. Overall, LAP enables dynamic manipulation with real-time adaptation and successfully transfer across heterogeneous robot platforms using the same human demonstration videos.

cs.RO