This paper delves into the challenges of sim-to-real transfer in robotics, focusing on Reinforcement Learning techniques, control architectures, simulator responses, and trained model movements. The study showcases successful sim-to-real transfer using TIAGo and Nvidia simulators, highlighting key differences between simulated and real setups.
Reinforcement Learning (RL) techniques are crucial for autonomous applications in robotics, particularly for object manipulation and navigation tasks. Gathering data to train models can be costly and time-consuming, necessitating the use of multiple robots running simultaneously. However, RL on real robots still requires supervision to prevent unexpected scenarios.
To expedite the training process, various RL libraries like OpenAI's Gymnasium and Stable-baselines were developed. Simulators such as Mujoco were introduced to simulate robot models economically but not necessarily faster. With the emergence of GPU-supported simulators like IsaacGym and IsaacSim from Nvidia, the focus shifted to bridging the gap between simulation and reality through sim-to-real transfer.
The study explores the use case of TIAGo mobile manipulator robot in simulating physics with simplified meshes for faster simulations. Control pipelines differ between Isaac Gym and Isaac Sim, affecting how robots react to control inputs. Evaluating simulator responses revealed differences in joint movements between simulated environments and real setups.
Training models with reward functions showed varying movements between simulated environments and real setups despite similar training epochs. While promising achievements were demonstrated in sim-to-real transfer using TIAGo and Nvidia simulators, identified issues need addressing to reduce the gap further.
toiselle kielelle
lähdeaineistosta
arxiv.org
Syvällisempiä Kysymyksiä