robosuite: A Modular Simulation Framework and Benchmark for Robot Learning
arXiv:2009.12293
Abstract
robosuite is a simulation framework for robot learning powered by the MuJoCo physics engine. It offers a modular design for creating robotic tasks as well as a suite of benchmark environments for reproducible research. This paper discusses the key system modules and the benchmark environments of our new release robosuite v1.5.
For more information, please visit https://robosuite.ai
References in corpus (3)
Cited by in corpus (36)
- Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments
- A Survey of Zero-shot Generalisation in Deep Reinforcement Learning
- What Matters in Learning from Offline Human Demonstrations for Robot Manipulation
- iGibson 2.0: Object-Centric Simulation for Robot Learning of Everyday Household Tasks
- Residual Learning from Demonstration: Adapting DMPs for Contact-rich Manipulation
- iGibson 1.0: a Simulation Environment for Interactive Tasks in Large Realistic Scenes
- BEHAVIOR: Benchmark for Everyday Household Activities in Virtual, Interactive, and Ecological Environments
- Conservative Safety Critics for Exploration
- Benchmarking the Sim-to-Real Gap in Cloth Manipulation
- ManiSkill: Generalizable Manipulation Skill Benchmark with Large-Scale Demonstrations
- Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors
- A Survey of Embodied AI: From Simulators to Research Tasks
- Knowledge Transfer for Cross-Domain Reinforcement Learning: A Systematic Review
- On the Role of the Action Space in Robot Manipulation Learning and Sim-to-Real Transfer
- ThriftyDAgger: Budget-Aware Novelty and Risk Gating for Interactive Imitation Learning
- Geometric Impedance Control on SE(3) for Robotic Manipulators
- SECANT: Self-Expert Cloning for Zero-Shot Generalization of Visual Policies
- 3DB: A Framework for Debugging Computer Vision Models
- Contact-rich SE(3)-Equivariant Robot Manipulation Task Learning via Geometric Impedance Control
- Human-in-the-Loop Imitation Learning using Remote Teleoperation
- CORA: Benchmarks, Baselines, and Metrics as a Platform for Continual Reinforcement Learning Agents
- Lifelong Robotic Reinforcement Learning by Retaining Experiences
- rl_reach: Reproducible Reinforcement Learning Experiments for Robotic Reaching Tasks
- Continual Model-Based Reinforcement Learning with Hypernetworks
- Zero-shot Task Adaptation using Natural Language
- S4RL: Surprisingly Simple Self-Supervision for Offline Reinforcement Learning
- Leveraging Demonstrator-perceived Precision for Safe Interactive Imitation Learning of Clearance-limited Tasks
- Guided Imitation of Task and Motion Planning
- Multi-Modal Mutual Information (MuMMI) Training for Robust Self-Supervised Deep Reinforcement Learning
- Building Minimal and Reusable Causal State Abstractions for Reinforcement Learning
- Koopman Q-learning: Offline Reinforcement Learning via Symmetries of Dynamics
- Learning Robotic Manipulation Skills Using an Adaptive Force-Impedance Action Space
- Learning Time-Invariant Reward Functions through Model-Based Inverse Reinforcement Learning
- Learning a Skill-sequence-dependent Policy for Long-horizon Manipulation Tasks
- Robust Robotic Control from Pixels using Contrastive Recurrent State-Space Models
- Bottom-Up Skill Discovery from Unsegmented Demonstrations for Long-Horizon Robot Manipulation