ManiSkill-HAB: A Benchmark for Low-Level Manipulation in Home Rearrangement Tasks

Figure 1: Live-rendered frames taken from ManiSkill-HAB environments while running policy rollouts with skill chaining. High-quality benchmarks are the foundation for embodied AI research, enabling significant advancements in long-horizon navigation, manipulation and rearrangement tasks. However, as frontier tasks in robotics get more advanced, they require faster simulation speed, more intricate test environments, and larger demonstration datasets. To this end, we present MS-HAB, a holistic benchmark for lowlevel manipulation and in-home object rearrangement. First, we provide a GPUaccelerated implementation of the Home Assistant Benchmark (HAB). We support realistic low-level control and achieve over 3x the speed of previous magical grasp implementations at similar GPU memory usage. Second, we train extensive reinforcement learning (RL) and imitation learning (IL) baselines for future work to compare against. Finally, we develop a rule-based trajectory filtering system to sample specific demonstrations from our RL policies which match predefined criteria for robot behavior and safety. Combining demonstration filtering with our fast environments enables efficient, controlled data generation at scale. An important goal of embodied AI is to create robots that can solve manipulation tasks in home-scale environments. Recently, faster and more realistic simulation, home-scale rearrangement benchmarks, and large robot datasets have provided important platforms to accelerate research towards this goal. However, there remains a need for all of these features in one unified benchmark. Fast Manipulation Simulation with Realistic Physics and Rendering: Using ManiSkill3 (Tao et al., 2024), we implement a GPU-accelerated version of the HAB (Szot et al., 2021), an apartmentscale rearrangement benchmark containing three long-horizon tasks using the Fetch mobile manipulator (ZebraTechnologies, 2024). While the original HAB uses magical grasp (teleport closest object within 15cm to the gripper), we require realistic grasping.