ManiSkill3: GPU Parallelized Robotics Simulation and Rendering for Generalizable Embodied AI (2410.00425v1)

Abstract: Simulation has enabled unprecedented compute-scalable approaches to robot learning. However, many existing simulation frameworks typically support a narrow range of scenes/tasks and lack features critical for scaling generalizable robotics and sim2real. We introduce and open source ManiSkill3, the fastest state-visual GPU parallelized robotics simulator with contact-rich physics targeting generalizable manipulation. ManiSkill3 supports GPU parallelization of many aspects including simulation+rendering, heterogeneous simulation, pointclouds/voxels visual input, and more. Simulation with rendering on ManiSkill3 can run 10-1000x faster with 2-3x less GPU memory usage than other platforms, achieving up to 30,000+ FPS in benchmarked environments due to minimal python/pytorch overhead in the system, simulation on the GPU, and the use of the SAPIEN parallel rendering system. Tasks that used to take hours to train can now take minutes. We further provide the most comprehensive range of GPU parallelized environments/tasks spanning 12 distinct domains including but not limited to mobile manipulation for tasks such as drawing, humanoids, and dextrous manipulation in realistic scenes designed by artists or real-world digital twins. In addition, millions of demonstration frames are provided from motion planning, RL, and teleoperation. ManiSkill3 also provides a comprehensive set of baselines that span popular RL and learning-from-demonstrations algorithms.

• The paper presents a state-of-the-art GPU parallelized simulation and rendering framework that accelerates RL training up to 1000 times faster than existing platforms.
• The methodology supports a comprehensive set of tasks and robotic embodiments, offering over 20 robots and 12 task categories with minimal memory overhead.
• The work emphasizes heterogeneous simulation and a unified API that streamlines task creation, paving the way for scalable dataset generation and embodied AI research.

ManiSkill3: GPU Parallelized Robotics Simulation and Rendering for Generalizable Embodied AI

ManiSkill3 represents a significant advancement in the simulation of robotics tasks, specifically aimed at enabling generalizable embodied AI. The paper details the contributions of ManiSkill3 in several key areas such as GPU parallelized simulation and rendering, support for a comprehensive range of environments and tasks, heterogeneous simulation, and a user-friendly API for task creation.

Core Contributions

1. State-of-the-Art GPU Parallelized Simulation and Rendering:

ManiSkill3 distinguishes itself with its ability to execute Reinforcement Learning (RL) algorithms like Proximal Policy Optimization (PPO) up to 1000 times faster than other platforms. It achieves up to 30,000+ frames per second (FPS) due to its minimal Python/PyTorch overhead, GPU-based simulation, and the SAPIEN parallel rendering system. These improvements allow tasks that previously took hours to train to be completed in minutes. The system's GPU memory usage is also optimized, consuming 2-3 times less memory than comparable platforms.

2. Comprehensive Range of Environments and Robots:

ManiSkill3 supports 12 distinct task categories, including mobile manipulation, room-scale scenes, humanoid interactions, and dextrous manipulation. It offers more than 20 different robot embodiments, facilitating diverse robotic tasks right out of the box. Extensive documentation and tutorials help users customize and expand the repository of simulated tasks and robots.

3. Heterogeneous Simulation for Generalizable Learning:

A standout feature is the environment's ability to simulate and render vastly different objects and scenes in parallel environments. This heterogeneity is facilitated by a data-oriented system design and a straightforward API managing GPU memory of objects and articulations with different degrees of freedom.

4. Simple Unified API:

ManiSkill3's API is designed for simplicity and flexibility, making it easy for users to build and manage GPU-simulated tasks. It includes object-oriented APIs and streamlines operations such as domain randomization, trajectory replay, and action space conversion. This user-centric design removes much of the complexity found in other simulation frameworks.

5. Scalable Dataset Generation Pipeline:

The pipeline allows for the generation of large datasets from a few demonstrations. It uses online imitation learning algorithms to generalize neural network policies, rolling out to create extensive datasets from minimal initial data.

Related Work

ManiSkill3 integrates and surpasses features found in several existing frameworks:

• Isaac Lab and Mujoco (MJX): While these frameworks have made strides in GPU parallelized simulations, their scope is often limited to narrower task ranges and lacks support for highly parallelized rendering. ManiSkill3 builds on these by reducing simulation and rendering overhead and enabling more extensive memory efficiency.
• Robotics Datasets: Existing datasets like Open-X and DROID are human-labor-intensive and difficult to scale. In contrast, ManiSkill3 generates large-scale demonstrations using motion planning, reinforcement learning, and advanced online imitation learning techniques, making data generation faster and less labor-intensive.

Implications and Future Developments

The implications of ManiSkill3 are significant for both practical and theoretical advancements in AI. Practically, the ability to train complex manipulation tasks faster and more efficiently can lead to more robust real-world deployments of robotic systems. Theoretically, the framework's support for heterogeneous simulation environments promotes research in generalizable learning algorithms, paving the way for more versatile AI systems.

In the future, developments could focus on enhancing the realism of simulations through improved rendering techniques and expanding the range of supported robots and environments. Further integration with advanced learning algorithms could also be explored to push the boundaries of what can be achieved in robotic autonomy and manipulation.

Conclusion

ManiSkill3 sets a new standard for GPU-parallelized robotics simulation and rendering, offering a highly efficient and comprehensive platform for embodied AI research. Its ease of use, combined with support for a wide range of tasks and heterogeneous environments, democratizes access to scalable robot learning and simulation, encouraging further advancements in the field.