{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/7"}],"enrichment":{"capability":"robosuite is a MuJoCo-powered simulation framework for building, benchmarking, and training robot control policies through reinforcement learning and imitation learning.","skillfed_tags":["robotics-simulation","reinforcement-learning","benchmark-environments"],"use_cases":["Train reinforcement learning agents on standardized manipulation tasks with reproducible benchmark environments.","Prototype custom robot environments by composing robot models, arenas, and parameterized objects procedurally.","Collect and replay human teleoperation demonstrations for imitation learning workflows.","Evaluate different robot embodiments and controller types on the same task for comparative research.","Generate synthetic training data with multi-modal sensors (RGB, depth, state) for vision-based robot learning."],"what_it_does":"robosuite is a modular simulation framework built on the MuJoCo physics engine, designed for researchers and practitioners developing robot learning algorithms. It provides a standardized suite of manipulation tasks, procedural environment generation, multiple robot embodiments (including humanoids as of v1.5), and a range of control abstractions\u2014from joint-space velocity control to whole-body controllers. The framework also integrates teleoperation devices, multi-modal sensors (RGB, depth, proprioception), and photorealistic rendering.\n\nThe package is actively maintained by researchers at Stanford, UT Austin, and NVIDIA, and is intended to lower barriers to entry for robot learning research by offering reproducible benchmarks, modular APIs for custom environment design, and utilities for collecting and replaying human demonstrations. It supports current Python versions and has no known security vulnerabilities.","worth_installing":"Yes, if you are doing robot learning research or prototyping. robosuite is actively maintained, has low install friction, zero known vulnerabilities, and is widely used in academic robotics labs. The main caveat is that the license treatment is unclear\u2014verify the license terms before use in proprietary projects. The 12 runtime dependencies are standard and well-maintained."},"id":"robosuite","links":{"html":"https://skillfed.io/packages/robosuite","md":"https://skillfed.io/packages/robosuite.md","pypi":"https://pypi.org/project/robosuite/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2025-12-24","license_spdx":null,"license_treatment":"unclear","name":"robosuite","python_support":"supports_current","summary":"robosuite: A Modular Simulation Framework and Benchmark for Robot Learning"},"popularity":{"monthly_downloads":252205,"position":8551,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.5.2"}