Arth Shukla
@arth.website
PhDing @HaoSuLabUCSD and @Hillbot | Robot Learning and Computer Vision | 2 Cat 2 Dad | arth.website
ManiSkill-HAB is my first first-author work, and it would not have been possible without the mentorship, guidance, and support of @stonet2000.bsky.social and Hao Su, and I'm incredibly thankful! I'm also thankful for the feedback provided by the Hillbot and Hao Su Lab teams.
December 19, 2024 at 10:49 PM
ManiSkill-HAB is my first first-author work, and it would not have been possible without the mentorship, guidance, and support of @stonet2000.bsky.social and Hao Su, and I'm incredibly thankful! I'm also thankful for the feedback provided by the Hillbot and Hao Su Lab teams.
🔓 Everything is open source!
• Paper: arxiv.org/abs/2412.13211
• Code: github.com/arth-shukla/mshab
• Models: huggingface.co/arth-shukla/mshab_checkpoints
• Datasets: arth-shukla.github.io/mshab/#dataset-section
We hope our environments, baselines, and dataset are useful to the community :)
(5/5)
• Paper: arxiv.org/abs/2412.13211
• Code: github.com/arth-shukla/mshab
• Models: huggingface.co/arth-shukla/mshab_checkpoints
• Datasets: arth-shukla.github.io/mshab/#dataset-section
We hope our environments, baselines, and dataset are useful to the community :)
(5/5)
ManiSkill-HAB: A Benchmark for Low-Level Manipulation in Home Rearrangement Tasks
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 r...
arxiv.org
December 19, 2024 at 10:47 PM
🔓 Everything is open source!
• Paper: arxiv.org/abs/2412.13211
• Code: github.com/arth-shukla/mshab
• Models: huggingface.co/arth-shukla/mshab_checkpoints
• Datasets: arth-shukla.github.io/mshab/#dataset-section
We hope our environments, baselines, and dataset are useful to the community :)
(5/5)
• Paper: arxiv.org/abs/2412.13211
• Code: github.com/arth-shukla/mshab
• Models: huggingface.co/arth-shukla/mshab_checkpoints
• Datasets: arth-shukla.github.io/mshab/#dataset-section
We hope our environments, baselines, and dataset are useful to the community :)
(5/5)
📊 We're releasing a massive dataset and generation tools to help the community solve these tasks
• 466GB of RGBD + state data
• 44K episodes
• 8.8M transitions
• Detailed event labeling + trajectory filtering
Download: arth-shukla.github.io/mshab/#dataset-section
(4/5)
• 466GB of RGBD + state data
• 44K episodes
• 8.8M transitions
• Detailed event labeling + trajectory filtering
Download: arth-shukla.github.io/mshab/#dataset-section
(4/5)
December 19, 2024 at 10:47 PM
📊 We're releasing a massive dataset and generation tools to help the community solve these tasks
• 466GB of RGBD + state data
• 44K episodes
• 8.8M transitions
• Detailed event labeling + trajectory filtering
Download: arth-shukla.github.io/mshab/#dataset-section
(4/5)
• 466GB of RGBD + state data
• 44K episodes
• 8.8M transitions
• Detailed event labeling + trajectory filtering
Download: arth-shukla.github.io/mshab/#dataset-section
(4/5)
🤖 We provide extensive RL & IL baselines and model checkpoints for whole-body control, tackling complex, very long-horizon rearrangement tasks. Each task chains multiple skills (Pick, Place, Open, Close) with simultaneous navigation & manipulation. (3/5)
December 19, 2024 at 10:46 PM
🤖 We provide extensive RL & IL baselines and model checkpoints for whole-body control, tackling complex, very long-horizon rearrangement tasks. Each task chains multiple skills (Pick, Place, Open, Close) with simultaneous navigation & manipulation. (3/5)
⚡️ MS-HAB provides a GPU-accelerated implementation of the Home Assistant Benchmark (HAB) with realistic low-level control for successful grasping, manipulation, & interaction, all while achieving 3x the speed of prior work at similar GPU memory usage. (2/5)
December 19, 2024 at 10:46 PM
⚡️ MS-HAB provides a GPU-accelerated implementation of the Home Assistant Benchmark (HAB) with realistic low-level control for successful grasping, manipulation, & interaction, all while achieving 3x the speed of prior work at similar GPU memory usage. (2/5)