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Increases the capacity of physics simulation buffers to prevent errors when collisions or objects exceed initial allocations.
Distinct from GPU Buffer Allocators: Distinct from GPU Buffer Allocators: focuses on resolving physics simulation buffer overflows rather than general GPU memory allocation.
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Isaac Lab is an open-source framework for training robot policies in physically simulated environments, supporting both single-agent and multi-agent reinforcement learning. It is built on an Omniverse-PhysX simulation backend that models rigid bodies, articulated systems, deformable objects, and sensors, and provides a task-based environment configuration system where each training environment is defined as a modular class specifying observation spaces, action spaces, reward functions, and termination conditions. The framework distinguishes itself through an RL-library abstraction layer that
Increases physics simulation buffer capacity to prevent overflow errors from collisions or objects.