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About this paper
Roland Hafner, Tim Hertweck, Philipp Klöppner, Michael Bloesch, Michael Neunert, Markus Wulfmeier, Saran Tunyasuvunakool, Nicolas Heess and 1 more
The authors apply sensor-based rewards and multi-task learning to locomotion across different robot designs. They calculate balance and movement rewards from onboard sensing, then reuse the same learning recipe across morphologies. In simulation, Daisy4 learned sparse ReachTarget in 800 episodes, while reward-only training did not solve it within 20,000; real-robot learning was demonstrated on one quadruped, not as zero-shot transfer.
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