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BayRnTune: Adaptive Bayesian Domain Randomization via Strategic Fine-tuning
Domain randomization (DR), which entails training a policy with randomized dynamics, has proven to be a simple yet effective algorithm for reducing the gap between simulation and the real world.
Cascaded Compositional Residual Learning for Complex Interactive Behaviors
Real-world autonomous missions often require rich interaction with nearby objects, such as doors or switches, along with effective navigation.
Estimating Mass Distribution of Articulated Objects using Non-prehensile Manipulation
Using our method, we train a robotic arm to estimate the mass distribution of an object with moving parts (e. g. an articulated rigid body system) by pushing it on a surface with unknown friction properties.
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