… In this paper, we present a method and empirical evidence towards a robot learning … robotic
manipulation policies to new variations by fine-tuning via off-policy reinforcement learning, …

… fine-tuning for reinforcement learning from high-dimensional observations in the context of
realistic robot … Recent offline model-free approaches successfully use online fine-tuning to …

… robot. This leads to a frequent tension in robotic learning: how can we learn generalizable
robotic … achieves the best performance when finetuned with 300 trajectories from the test robot. …

… 1: We demonstrate real-world improvement through fine-tuning multiple skills to various
real-world environments. The robot learns to walk back and forth on grass (top left) and side-step …

… to many new tasks without destruction from fine-tuning. Second, the columns may be … , or
simply to improve learning speed when transferring to the real robot. Third, progressive nets …

… -task pretraining, followed by fine-tuning on novel tasks, performs … robot learning to
shift towards 54 more challenging benchmarks, and involve multi-task pretraining with fine-tuning …

… finetuning. In comparison, the MLP backbone pretrained on the same data is unable to
generalize zeroshot to unseen tasks. By pretraining on even more diverse task-agnostic data, we …

… -LCA dataset, we compare to a common domain adaptation baseline: fine-tuning the model
learned on Lab-LCA with 5K home grasps (‘Fine-tuned’ in Table 1). We notice that this is …

… Robots are increasingly learning complex skills in simulation… iterative residual tuning (IRT),
a deep learning system … IRT learns to estimate the parameter difference between two …

… Abstract: As researchers teach robots to perform more and more … learning-based method
to directly tune the parameters of one model to match another using an iterative residual tuning …