@article {5541,
	title = {RESPRECT: Speeding-up Multi-Fingered Grasping With Residual Reinforcement LearningRESPRECT: Speeding-Up Multi-Fingered Grasping With Residual Reinforcement Learning_supp1-3363532.mp4},
	journal = {IEEE Robotics and Automation Letters},
	volume = {9},
	year = {2024},
	month = {04/2024},
	pages = {3045 - 3052},
	abstract = {<p>Deep Reinforcement Learning (DRL) has proven effective in learning control policies using robotic grippers, but much less practical for solving the problem of grasping with dexterous hands {\textendash} especially on real robotic platforms {\textendash} due to the high dimensionality of the problem. In this letter, we focus on the multi-fingered grasping task with the anthropomorphic hand of the iCub humanoid. We propose the RESidual learning with PREtrained CriTics (RESPRECT) method that, starting from a policy pre-trained on a large set of objects, can learn a residual policy to grasp a novel object in a fraction ( \~{}5{\texttimes} faster) of the timesteps required to train a policy from scratch, without requiring any task demonstration. To our knowledge, this is the first Residual Reinforcement Learning (RRL) approach that learns a residual policy on top of another policy pre-trained with DRL. We exploit some components of the pre-trained policy during residual learning that further speed-up the training. We benchmark our results in the iCub simulated environment, and we show that RESPRECT can be effectively used to learn a multi-fingered grasping policy on the real iCub robot.</p>
},
	doi = {10.1109/LRA.2024.3363532},
	url = {https://ieeexplore.ieee.org/document/10423830/},
	author = {Ceola, Federico and Rosasco, Lorenzo and Natale, Lorenzo and Ceola, Federico}
}
