robosuite: A Modular Simulation Framework and Benchmark for Robot Learning
Pith reviewed 2026-05-12 22:31 UTC · model grok-4.3
The pith
robosuite is a modular simulation framework powered by MuJoCo that supplies benchmark environments for reproducible robot learning research.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
The authors establish that robosuite v1.5 delivers key system modules supporting modular task creation alongside a suite of benchmark environments, enabling researchers to define custom robotic tasks and run reproducible learning experiments without rebuilding simulation infrastructure from scratch.
What carries the argument
The modular system modules for assembling robotic tasks, combined with the provided suite of benchmark environments.
If this is right
- Researchers can compose new robotic tasks by combining existing modules instead of starting from zero.
- Standard benchmark environments allow direct side-by-side comparison of different learning algorithms.
- Reproducible simulation setups reduce the time spent on infrastructure and increase time available for algorithm development.
- Consistent environments support cumulative progress because results from one paper can be verified or extended by others.
Where Pith is reading between the lines
- Widespread use could reduce duplication of effort across labs by providing a shared simulation base.
- The same modular structure might later support easier sim-to-real transfer once real-robot interfaces are added.
- Benchmark results could serve as a common reference point for comparing learning methods that currently rely on private environments.
Load-bearing premise
That researchers will adopt the modular architecture and benchmark environments without needing to write substantial additional custom code for their own tasks.
What would settle it
A survey or usage study in which most researchers report that they must still implement large amounts of custom simulation code to match their experimental needs, or in which benchmark results prove difficult to reproduce across independent implementations.
read the original 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.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper introduces robosuite v1.5, a modular simulation framework for robot learning powered by the MuJoCo physics engine. It describes the key system modules for task creation and presents a suite of benchmark environments intended to support reproducible research in the field.
Significance. If the described modular architecture and benchmarks function as outlined, the framework could provide a standardized platform that reduces the need for custom simulation code, thereby improving reproducibility across robot learning studies. The release of an open tool with explicit benchmark support is a practical contribution to the community.
minor comments (3)
- [Abstract] Abstract: the claim that the framework offers 'a suite of benchmark environments for reproducible research' would be strengthened by briefly noting the specific tasks included (e.g., manipulation, locomotion) and any quantitative validation of their stability or fidelity.
- The manuscript should include a dedicated section or table comparing robosuite v1.5 features against prior versions or alternative simulators (e.g., PyBullet, Gazebo) to clarify incremental advances.
- Ensure that all module descriptions cite the corresponding source files or API references so readers can directly inspect the implementation.
Simulated Author's Rebuttal
We thank the referee for the positive summary and significance assessment of our work on robosuite v1.5. The recommendation for minor revision is noted. As no specific major comments were provided in the report, we have no substantive points to address and believe the manuscript requires no technical revisions.
Circularity Check
No circularity: purely descriptive software framework paper
full rationale
The manuscript is a software release note for robosuite v1.5. It describes the modular architecture, MuJoCo integration, task-creation utilities, and benchmark environments without any derivations, equations, fitted parameters, predictions, or uniqueness theorems. No load-bearing self-citations or ansatzes appear; the central claim is simply that the described interfaces exist and are exposed. This is self-contained descriptive documentation rather than a chain of inferences that could reduce to its own inputs.
Axiom & Free-Parameter Ledger
Forward citations
Cited by 60 Pith papers
-
TAVIS: A Benchmark for Egocentric Active Vision and Anticipatory Gaze in Imitation Learning
TAVIS is a released benchmark showing active vision improves imitation learning in a task-dependent manner, multi-task policies struggle with shifts, and imitation produces human-like anticipatory gaze.
-
RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
RoboLab is a new simulation benchmark with 120 tasks across visual, procedural, and relational axes that quantifies generalization gaps and perturbation sensitivity in task-generalist robotic policies.
-
BEHAVIOR-1K: A Human-Centered, Embodied AI Benchmark with 1,000 Everyday Activities and Realistic Simulation
BEHAVIOR-1K introduces a benchmark of 1,000 human everyday activities in realistic simulated scenes together with the OMNIGIBSON physics simulator to evaluate embodied AI.
-
LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning
LIBERO is a new benchmark for lifelong robot learning that evaluates transfer of declarative, procedural, and mixed knowledge across 130 manipulation tasks with provided demonstration data.
-
OopsieVerse: A Safety Benchmark with Damage-Aware Simulation for Robot Manipulation
OOPSIEVERSE is a new damage-aware simulation benchmark for household robot manipulation that converts contact, thermal, and fluid signals into task-agnostic damage metrics and demonstrates uses in safer policy learnin...
-
DuoBench: A Reproducible Benchmark for Bimanual Manipulation in Simulation and the Real World
DuoBench introduces eleven bimanual manipulation tasks with stage-based evaluation and human datasets to benchmark imitation-learning and vision-language-action policies on dual-arm robots in sim and real settings.
-
VoLo: A Physical Orchestrator for Open-Vocabulary Long-Horizon Manipulation
VoLoAgent uses a VLM to steer heterogeneous robot capabilities as interruptible tools for long-horizon manipulation and introduces the RoboVoLo benchmark, claiming substantial outperformance over single VLA/VLM or too...
-
HapTile: A Haptic-Informed Vision-Tactile-Language-Action Dataset for Contact-Rich Imitation Learning
HapTile introduces a visuotactile dataset with haptic-informed teleoperation for language-conditioned contact-rich manipulation tasks and provides baseline policy benchmarks.
-
Dream.exe: Can Video Generation Models Dream Executable Robot Manipulation?
Dream.exe evaluates 8 video generation models on 101 manipulation tasks by converting generated videos into executable robot trajectories in a simulator, finding measurable success rates that visual metrics do not predict.
-
Same Weights, Different Robot: A Deployment Safety View of VLA Policies
The paper identifies a deployment safety gap in VLA policies where identical checkpoints can be executable-inequivalent due to action metadata mismatches, supported by a derived closed-form transform and empirical dri...
-
CapVector: Learning Transferable Capability Vectors in Parametric Space for Vision-Language-Action Models
Capability vectors extracted from parameter differences between standard and auxiliary-finetuned VLA models can be merged into pretrained weights to match auxiliary-training performance while reducing computational ov...
-
CoRAL: Contact-Rich Adaptive LLM-based Control for Robotic Manipulation
CoRAL lets LLMs act as adaptive cost designers for motion planners while using VLM priors and online identification to handle unknown physics, achieving over 50% higher success rates than baselines in unseen contact-r...
-
Atomic-Probe Governance for Skill Updates in Compositional Robot Policies
A cross-version swap protocol reveals dominant skills that swing composition success by up to 50 percentage points, and an atomic probe with selective revalidation governs updates at lower cost than always re-testing ...
-
HANDFUL: Sequential Grasp-Conditioned Dexterous Manipulation with Resource Awareness
HANDFUL learns resource-aware grasps using finger contact rewards and curriculum learning to improve success on sequential dexterous tasks in simulation and on a real LEAP hand.
-
Agent-Centric Observation Adaptation for Robust Visual Control under Dynamic Perturbations
ACO-MoE recovers 95.3% of clean-input performance in visual control tasks under Markov-switching corruptions by routing restoration experts and anchoring representations to clean foreground masks.
-
Agent-Centric Observation Adaptation for Robust Visual Control under Dynamic Perturbations
ACO-MoE employs agent-centric mixture-of-experts to decouple task-relevant features from dynamic visual perturbations in RL, recovering 95.3% of clean performance on the new VDCS benchmark.
-
BiCoord: A Bimanual Manipulation Benchmark towards Long-Horizon Spatial-Temporal Coordination
BiCoord is a new benchmark for long-horizon tightly coordinated bimanual manipulation that includes quantitative metrics and shows existing policies like DP, RDT, Pi0 and OpenVLA-OFT struggle on such tasks.
-
Towards Generalizable Robotic Manipulation in Dynamic Environments
DOMINO dataset and PUMA architecture enable better dynamic robotic manipulation by incorporating motion history, delivering 6.3% higher success rates than prior VLA models.
-
ST-BiBench: Benchmarking Multi-Stream Multimodal Coordination in Bimanual Embodied Tasks for MLLMs
ST-BiBench reveals a coordination paradox in which MLLMs show strong high-level strategic reasoning yet fail at fine-grained 16-dimensional bimanual action synthesis and multi-stream fusion.
-
MIMIC-D: Multi-modal Imitation for MultI-agent Coordination with Decentralized Diffusion Policies
MIMIC-D enables multi-modal multi-agent coordination via joint training of decentralized diffusion policies using only local information.
-
Voyager: An Open-Ended Embodied Agent with Large Language Models
Voyager achieves superior lifelong learning in Minecraft by combining an automatic exploration curriculum, a library of executable skills, and iterative LLM prompting with environment feedback, yielding 3.3x more uniq...
-
Freeform Preference Learning for Robotic Manipulation
Freeform Preference Learning trains language-conditioned multi-axis reward models from human pairwise preferences to produce steerable and compositional robot policies that outperform sparse and binary-preference base...
-
Sequential Planning via Anchored Robotic Keypoints
SPARK reaches 43.7% success on six LIBERO-PRO cells by LLM-generated typed behavior trees plus multi-prompt perception and recovery, more than doubling CaP-Agent0 and VLA baselines.
-
UniFS: Unified Fast-to-Slow Hierarchical Architecture for Vision-Language-Action Models
UniFS achieves 98.3% success on LIBERO with 2.1x lower latency than prior fast-slow VLA models by stratifying VLM layer update frequencies, inverting latent interactions, and applying multi-level supervision.
-
Inductive Generalization for Robotic Manipulation
The paper introduces an inductive generalization evaluation protocol for manipulation policies and shows that SOTA vision-language-action models fail on progressively harder task variants.
-
Playful Agentic Robot Learning
RATs agents generate and solve their own exploratory tasks during play, distill successful code into a skill library, and reuse it to improve held-out task performance by 20.6 and 17.0 points on two benchmarks.
-
AnnotateAnything: Automatic Annotation of 3D Assets for Robot Manipulation
AnnotateAnything converts passive 3D assets into manipulation-ready assets by combining vision-language reasoning for semantics with parallel physics pipelines for executable action annotations such as grasps and arti...
-
SafeDojo: Safe Reinforcement Learning for VLA via Interactive World Model
SafeDojo is a new world model-based safe RL framework for VLA that outperforms baselines on SafeLIBERO and real robot tasks.
-
An Embodied Simulation Platform, Benchmark, and Data-Efficient Augmentation Framework for Wet-Lab Robotics
Pipette supplies an open wet-lab simulation platform, 11-task benchmark, and perturbation-based augmentation pipeline that raises VLA success rates on sample handling and device tasks from limited demonstrations.
-
What Demonstration Curation Metrics Do to Your Policy
On a LIBERO pick-and-place task with gripper defects, curation metrics with highest defect-detection AUROC produce the worst policies while lower-AUROC metrics nearly match the oracle, and many metrics rely on episode...
-
iMaC: Translating Actions into Motion and Contact Images for Embodied World Models
iMaC introduces image-based action tokens in a dual-branch architecture to improve future state prediction and control in embodied world models over vector-based baselines.
-
Difference-Aware Retrieval Policies for Imitation Learning
DARP reparameterizes imitation learning around local neighborhood structure using k-NN expert states, actions, and relative distance vectors, delivering 15-46% gains over behavior cloning in control and manipulation tasks.
-
MoDex: A Diffusion Policy for Sequential Multi-Object Dexterous Grasping
MoDex is a diffusion policy conditioned on opposition space and point cloud, trained first by imitation learning then RL fine-tuning, that reports higher success rates than baselines for sequential multi-object dexter...
-
What Are We Actually Benchmarking in Robot Manipulation?
LIBERO and CALVIN fail multiple proposed diagnostics for shortcut solvability, statistical significance, overfitting, and data dependence, while a tiny 0.09B probe reaches near-SOTA on LIBERO.
-
CLAW: Learning Continuous Latent Action World Models via Adversarial Latent Regularization
CLAW is an end-to-end self-supervised method that learns semantically meaningful continuous latent actions and predictive world models from action-free videos to support imitation learning and goal-directed planning.
-
Set-Supervised Diffusion Policy: Learning Action-Chunking Diffusion through Corrections
SDP constructs sets of desired action-chunks from human correction pairs and trains diffusion policies to align with those sets, yielding better performance and robustness than standard behavior cloning on robotic tasks.
-
Drift Q-Learning
DriftQL is a single-pass offline RL algorithm using drift regularization that outperforms diffusion and flow policies on standard benchmarks.
-
RoboWits: Unexpected Challenges for Robotic Creative Problem Solving
RoboWits benchmark with 238 tasks shows pre-trained VLAs succeed on seed tasks but fail on mutated ones, highlighting brittleness in reasoning.
-
Frequency-Guided Action Diffusion via Sub-Frequency Manifold Traversal
FGO guides diffusion policy generation via expanding spectral bands on sub-frequency manifolds to improve action smoothness on 15 robotic manipulation tasks.
-
Behavior-Consistent Deep Reinforcement Learning
QED bounds cross-run KL divergence in Boltzmann policies by setting temperature proportional to Q-disagreement and reduces return variance by two orders of magnitude on 18 continuous-control tasks without performance loss.
-
Behavior-Consistent Deep Reinforcement Learning
QED sets state-dependent temperature proportional to double-critic disagreement to bound pairwise KL divergence between Boltzmann policies, cutting cross-run divergence by two orders of magnitude on 18 continuous-cont...
-
Beyond Action Residuals: Real-World Robot Policy Steering via Bottleneck Latent Reinforcement Learning
ZPRL adapts frozen flow-matching imitation policies via RL perturbations on a task-relevant bottleneck latent, yielding 33.7% higher average success on four real-world manipulation tasks than action-residual baselines.
-
COBALT: Crowdsourcing Robot Learning via Cloud-Based Teleoperation with Smartphones
COBALT provides scalable cloud infrastructure for crowdsourced robot teleoperation via smartphones, supporting concurrent users with low latency and enabling collection of a 7500+ demonstration dataset validated throu...
-
COBALT: Crowdsourcing Robot Learning via Cloud-Based Teleoperation with Smartphones
COBALT enables scalable crowdsourced teleoperation of robots using smartphones, supporting concurrent users with low latency and yielding a 7500+ demonstration dataset validated on imitation learning tasks.
-
DexJoCo: A Benchmark and Toolkit for Task-Oriented Dexterous Manipulation on MuJoCo
DexJoCo is a benchmark and toolkit with 11 functionally grounded tasks, 1.1K trajectories, and empirical benchmarks for task-oriented dexterous manipulation on MuJoCo.
-
Ada-Diffuser: Latent-Aware Adaptive Diffusion for Decision-Making
Ada-Diffuser is a causal diffusion model that jointly learns observed interaction structure and underlying latent dynamics from minimal observations for adaptive planning and policy learning.
-
Slot-MPC: Goal-Conditioned Model Predictive Control with Object-Centric Representations
Slot-MPC learns slot representations to build a differentiable object-centric dynamics model that supports efficient gradient-based MPC for robotic manipulation in novel situations.
-
HeteroGenManip: Generalizable Manipulation For Heterogeneous Object Interactions
A task-conditioned two-stage system decouples grasp localization from interaction trajectory planning using specialized foundation models to improve generalization across heterogeneous object types.
-
HeteroGenManip: Generalizable Manipulation For Heterogeneous Object Interactions
HeteroGenManip decouples grasp localization from interaction planning using task-conditioned foundation models and multi-model diffusion policies, delivering 31% average gains in broad simulation tasks and 36.7% in fo...
-
Kintsugi: Learning Policies by Repairing Executable Knowledge Bases
Kintsugi learns policies by repairing composable executable knowledge bases through agentic diagnosis, localized typed edits, and deterministic verification gates that admit only improvements.
-
BEACON: Cross-Domain Co-Training of Generative Robot Policies via Best-Effort Adaptation
BEACON uses discrepancy-aware importance reweighting to jointly train diffusion-based robot policies and source sample weights, improving performance over target-only and fixed-ratio baselines in cross-domain manipula...
-
BEACON: Cross-Domain Co-Training of Generative Robot Policies via Best-Effort Adaptation
BEACON uses discrepancy-aware importance reweighting to co-train generative robot policies from abundant source and limited target demonstrations, yielding better robustness and implicit feature alignment.
-
How to Utilize Failure Demo Data?: Effective Data Selection for Imitation Learning Using Distribution Differences in Attention Mechanism
The method uses attention discrepancy metrics on latent success-failure representations to select beneficial failure data for imitation learning, raising task success rates in simulations.
-
Atomic-Probe Governance for Skill Updates in Compositional Robot Policies
Empirical study on robosuite tasks reveals a dominant-skill effect in compositions and shows that an atomic probe approximates full revalidation for skill updates at much lower cost.
-
GS-Playground: A High-Throughput Photorealistic Simulator for Vision-Informed Robot Learning
GS-Playground delivers a high-throughput photorealistic simulator for vision-informed robot learning via parallel physics integrated with batch 3D Gaussian Splatting at 10^4 FPS and an automated Real2Sim workflow for ...
-
Visual-Tactile Peg-in-Hole Assembly Learning from Peg-out-of-Hole Disassembly
A visual-tactile RL method learns peg-in-hole assembly from reversed peg-out-of-hole disassembly trajectories, reaching 87.5% success on seen objects and 77.1% on unseen objects while lowering contact forces.
-
A Mechanistic Analysis of Sim-and-Real Co-Training in Generative Robot Policies
Sim-and-real co-training for robot policies is driven primarily by balanced cross-domain representation alignment and secondarily by domain-dependent action reweighting.
-
RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
RoboLab is a photorealistic simulation benchmark with 120 tasks and perturbation analysis to evaluate true generalization and robustness of robotic foundation models.
-
Learning Without Losing Identity: Capability Evolution for Embodied Agents
Embodied agents maintain a persistent identity while evolving capabilities via modular ECMs, raising simulated task success from 32.4% to 91.3% over 20 iterations with zero policy drift or safety violations.
-
Learning Without Losing Identity: Capability Evolution for Embodied Agents
Embodied agents maintain persistent identity while evolving modular capabilities through a closed-loop process, raising simulated task success from 32.4% to 91.3% with zero policy drift.
Reference graph
Works this paper leans on
-
[1]
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba. Openai gym. arXiv preprint arXiv:1606.01540, 2016
work page internal anchor Pith review Pith/arXiv arXiv 2016
-
[2]
CARLA: An Open Urban Driving Simulator
Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun. Carla: An open urban driving simulator. arXiv preprint arXiv:1711.03938, 2017
work page Pith review arXiv 2017
-
[3]
Surreal: Open-source 17 reinforcement learning framework and robot manipulation benchmark
Linxi Fan*, Yuke Zhu*, Jiren Zhu, Zihua Liu, Orien Zeng, Anchit Gupta, Joan Creus-Costa, Silvio Savarese, and Li Fei-Fei. Surreal: Open-source 17 reinforcement learning framework and robot manipulation benchmark. In Conference on Robot Learning , 2018
work page 2018
-
[4]
Soft Actor-Critic Algorithms and Applications
Tuomas Haarnoja, Aurick Zhou, Kristian Hartikainen, George Tucker, Se- hoon Ha, Jie Tan, Vikash Kumar, Henry Zhu, Abhishek Gupta, Pieter Abbeel, et al. Soft actor-critic algorithms and applications. arXiv preprint arXiv:1812.05905, 2018
work page internal anchor Pith review arXiv 2018
-
[5]
Deep reinforcement learning that matters
Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, and David Meger. Deep reinforcement learning that matters. In AAAI, 2018
work page 2018
-
[6]
Inertial properties in robotic manipulation: An object- level framework
Oussama Khatib. Inertial properties in robotic manipulation: An object- level framework. The international journal of robotics research , 14(1):19– 36, 1995
work page 1995
-
[7]
Reinforcement learning in robotics: A survey
Jens Kober, J Andrew Bagnell, and Jan Peters. Reinforcement learning in robotics: A survey. The International Journal of Robotics Research , 32(11):1238–1274, 2013
work page 2013
-
[8]
AI2-THOR: An Interactive 3D Environment for Visual AI
Eric Kolve, Roozbeh Mottaghi, Winson Han, Eli VanderBilt, Luca Weihs, Alvaro Herrasti, Daniel Gordon, Yuke Zhu, Abhinav Gupta, and Ali Farhadi. AI2-THOR: An interactive 3d environment for visual AI. arXiv preprint arXiv:1712.05474, 2017
work page internal anchor Pith review Pith/arXiv arXiv 2017
-
[9]
A review of robot learning for manipulation: Challenges, representations, and algorithms
Oliver Kroemer, Scott Niekum, and George Konidaris. A review of robot learning for manipulation: Challenges, representations, and algorithms. arXiv preprint arXiv:1907.03146 , 2019
-
[10]
Roboturk: A crowdsourcing platform for robotic skill learning through im- itation
Ajay Mandlekar, Yuke Zhu, Animesh Garg, Jonathan Booher, Max Spero, Albert Tung, Julian Gao, John Emmons, Anchit Gupta, Emre Orbay, et al. Roboturk: A crowdsourcing platform for robotic skill learning through im- itation. In Conference on Robot Learning , pages 879–893, 2018
work page 2018
-
[11]
Roberto Mart´ ın-Mart´ ın, Michelle A Lee, Rachel Gardner, Silvio Savarese, Jeannette Bohg, and Animesh Garg. Variable impedance control in end- effector space: An action space for reinforcement learning in contact-rich tasks. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , pages 1010–1017. IEEE, 2019
work page 2019
-
[12]
Recent advances in robot learning from demonstration
Harish Ravichandar, Athanasios S Polydoros, Sonia Chernova, and Aude Billard. Recent advances in robot learning from demonstration. Annual Review of Control, Robotics, and Autonomous Systems , 3, 2020
work page 2020
-
[13]
Reinforcement learning: An in- troduction
Richard S Sutton and Andrew G Barto. Reinforcement learning: An in- troduction. MIT press, 2018
work page 2018
-
[14]
Lillicrap and Nicolas Heess , title =
Yuval Tassa, Saran Tunyasuvunakool, Alistair Muldal, Yotam Doron, Siqi Liu, Steven Bohez, Josh Merel, Tom Erez, Timothy Lillicrap, and Nico- las Heess. dm control: Software and tasks for continuous control. arXiv preprint arXiv:2006.12983, 2020. 18
-
[15]
Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa. Mujoco: A physics engine for model-based control. In International Conference on Intelligent Robots and Systems , pages 5026–5033, 2012
work page 2012
-
[16]
Interactive gibson benchmark: A benchmark for interactive navigation in cluttered environments
Fei Xia, William B Shen, Chengshu Li, Priya Kasimbeg, Micael Edmond Tchapmi, Alexander Toshev, Roberto Mart´ ın-Mart´ ın, and Silvio Savarese. Interactive gibson benchmark: A benchmark for interactive navigation in cluttered environments. IEEE Robotics and Automation Letters , 5(2):713– 720, 2020
work page 2020
-
[17]
Mink: Python inverse kinematics based on MuJoCo, July 2024
Kevin Zakka. Mink: Python inverse kinematics based on MuJoCo, July 2024
work page 2024
-
[18]
Reinforcement and imitation learning for diverse visuomotor skills
Yuke Zhu, Ziyu Wang, Josh Merel, Andrei Rusu, Tom Erez, Serkan Cabi, Saran Tunyasuvunakool, J´ anos Kram´ ar, Raia Hadsell, Nando de Freitas, et al. Reinforcement and imitation learning for diverse visuomotor skills. Robotics: Science and Systems , 2018. 19
work page 2018
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.