Multi-Task Robotic Manipulation
Overview
The field aims to train robot policies that handle many manipulation tasks, rather than requiring a separate controller for each one. Common approaches include imitation learning with transformers or diffusion policies, vision-language-action models, reusable skill libraries, and reinforcement learning; some methods add geometric representations, world models, or structured task plans. Benchmarks such as LIBERO, RoboTwin, and RoboCasa test performance across task variations, while real-robot studies examine transfer, collaboration, and recovery from failures. A central challenge is balancing broad generalization and robustness with limited demonstrations, efficient models, and dependable performance outside the training setup.
Latest papers
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