FetchMan: Learning Visual Humanoid Loco-Manipulation Policies from Simulated Experiences
A new sim-to-real training pipeline enables humanoid robots to master complex navigation and object manipulation tasks.
Visual loco-manipulation policies that can generalize to novel scenes and objects have long been a goal of robotics research. However, today's data-hungry algorithms make collecting sufficient demonstrations a struggle for tabletop manipulation, and even more so for humanoids tha...
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FetchMan demonstrates that reinforcement learning can overcome the performance bottlenecks of imitation learning when training humanoids for complex navigation and object interaction tasks. This approach bridges the gap between simulated data and real-world deployment, marking a significant step toward developing versatile, general-purpose humanoid robots.
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