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arXiv · 1911.06636

Catch & Carry: Reusable Neural Controllers for Vision-Guided Whole-Body Tasks

Josh Merel·Saran Tunyasuvunakool·Arun Ahuja·Yuval Tassa·Leonard Hasenclever·Vu Pham·Tom Erez·Greg Wayne·Nicolas Heess
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Abstract

We address the longstanding challenge of producing flexible, realistic humanoid character controllers that can perform diverse whole-body tasks involving object interactions. This challenge is central to a variety of fields, from graphics and animation to robotics and motor neuroscience. Our physics-based environment uses realistic actuation and first-person perception -- including touch sensors and egocentric vision -- with a view to producing active-sensing behaviors (e.g. gaze direction), transferability to real robots, and comparisons to the biology. We develop an integrated neural-network based approach consisting of a motor primitive module, human demonstrations, and an instructed reinforcement learning regime with curricula and task variations. We demonstrate the utility of our approach for several tasks, including goal-conditioned box carrying and ball catching, and we characterize its behavioral robustness. The resulting controllers can be deployed in real-time on a standard PC. See overview video, https://youtu.be/2rQAW-8gQQk .

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Josh Merel, Saran Tunyasuvunakool, Arun Ahuja, Yuval Tassa, Leonard Hasenclever, Vu Pham, Tom Erez, Greg Wayne, Nicolas Heess. 2019-11-15. Catch & Carry: Reusable Neural Controllers for Vision-Guided Whole-Body Tasks. https://arxiv.org/abs/1911.06636

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