Concetti Chiave
The authors investigate policy-learning approaches for sim-to-real transfer in robotic manipulation using TIAGo and Nvidia simulators, emphasizing collision-less movement in both simulation and real environments.
Sintesi
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.
Statistiche
Models trained with data from multiple robots are more likely to work on real platforms.
Accumulated errors were smaller for Isaac Gym than for Isaac Sim.
The JointGroupPosition controller was employed for controlling TIAGo due to its similarity to Isaac's PD controller.
Citazioni
"RL techniques are especially useful for applications that require a certain degree of autonomy." - Content Source
"Simulating physics for objects that don’t have simple meshes is computationally expensive." - Content Source
"The first model that was trained takes the TIAGo mobile manipulator from its 'Home' position to a position where the arm is fully extended." - Content Source