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SAPIEN: A SimulAted Part-based Interactive ENvironment

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arxiv 2003.08515 v1 pith:AJTJBPRY submitted 2020-03-19 cs.CV cs.RO

classification cs.CVcs.RO
keywords environmentsapiensimulatedinteractionlearningtasksvisionachieve
verification ladder T0 review T1 audit T2 compute T3 formal
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Building home assistant robots has long been a pursuit for vision and robotics researchers. To achieve this task, a simulated environment with physically realistic simulation, sufficient articulated objects, and transferability to the real robot is indispensable. Existing environments achieve these requirements for robotics simulation with different levels of simplification and focus. We take one step further in constructing an environment that supports household tasks for training robot learning algorithm. Our work, SAPIEN, is a realistic and physics-rich simulated environment that hosts a large-scale set for articulated objects. Our SAPIEN enables various robotic vision and interaction tasks that require detailed part-level understanding.We evaluate state-of-the-art vision algorithms for part detection and motion attribute recognition as well as demonstrate robotic interaction tasks using heuristic approaches and reinforcement learning algorithms. We hope that our SAPIEN can open a lot of research directions yet to be explored, including learning cognition through interaction, part motion discovery, and construction of robotics-ready simulated game environment.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 22 citations worldwide. Full citation record

  1. Weights or Skills? A Survey of Robot-Learning Techniques: from Action-Predicting Weights to Robots that Write their Own Skills

    cs.RO 2026-08 conditional novelty 6.0 of 10

    A taxonomy of robot learning on a weights-versus-skills axis, with a five-rung self-improvement ladder whose top cell (feedback plus memory plus search) holds only a few recent systems.

  2. AXIS: A Growable Community-Driven Data Engine for Scalable Robot Manipulation

    cs.RO 2026-07 conditional novelty 5.0 of 10

    Pretraining π0.5 on the crowdsourced AXIS simulation dataset (207 tasks, 50K+ trajectories) raises downstream LIBERO-Plus success from 83.9% to 88.8% as the pretraining corpus grows from none to the full dataset.

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