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RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation

Yufei Wang, Zhou Xian, Feng Chen, Tsun-Hsuan Wang, Yian Wang, Katerina Fragkiadaki, Zackory Erickson, David Held, Chuang Gan

arXiv:2311.01455v3cs.ROcs.AIcs.CVcs.LG

TL;DR

RoboGen addresses the effort and limited scalability of manually constructing simulation tasks, assets, scenes, and training supervision for robot learning. It uses foundation models in a propose-generate-learn cycle to create diverse environments and task-specific supervision, and produces diversified skills across manipulation and locomotion. The system achieves an average success rate of 0.774 across 69 benchmarked tasks while generating a continuous stream of demonstrations.

  • Problem

    Constructing individualized simulation tasks, assets, scenes, and training supervisions requires substantial human effort, hindering scalable robotic skill learning.

  • Method

    RoboGen uses foundation models to propose skills, generate scenes and supervision, select learning approaches, and learn policies in a self-guided propose-generate-learn cycle.

  • Results

    RoboGen delivers diversified demonstrations across rigid and articulated manipulation, deformable manipulation, and legged locomotion, with an average success rate of 0.774 over 69 benchmarked tasks.

  • Takeaways & Limitations

    The fully generative pipeline can be queried endlessly to support automated large-scale robotic skill training and demonstration collection with minimal human supervision.

  • Takeaways & Limitations

    The paper notes a remaining gap associated with physically accurate simulation, despite anticipating that advances in simulation, domain randomization, and sensory rendering will narrow it.

Abstract

from arXiv · show

We present RoboGen, a generative robotic agent that automatically learns diverse robotic skills at scale via generative simulation. RoboGen leverages the latest advancements in foundation and generative models. Instead of directly using or adapting these models to produce policies or low-level actions, we advocate for a generative scheme, which uses these models to automatically generate diversified tasks, scenes, and training supervisions, thereby scaling up robotic skill learning with minimal human supervision. Our approach equips a robotic agent with a self-guided propose-generate-learn cycle: the agent first proposes interesting tasks and skills to develop, and then generates corresponding simulation environments by populating pertinent objects and assets with proper spatial configurations. Afterwards, the agent decomposes the proposed high-level task into sub-tasks, selects the optimal learning approach (reinforcement learning, motion planning, or trajectory optimization), generates required training supervision, and then learns policies to acquire the proposed skill. Our work attempts to extract the extensive and versatile knowledge embedded in large-scale models and transfer them to the field of robotics. Our fully generative pipeline can be queried repeatedly, producing an endless stream of skill demonstrations associated with diverse tasks and environments.

1. Introduction

RoboGen addresses the human effort required to construct simulation environments by using foundation and generative models to generate tasks, scenes, and training supervision. Its self-guided pipeline produces diverse robotic skills and demonstrations with minimal human involvement.

  • Motivation: Simulated environments accelerate robot learning through privileged low-level states, unlimited exploration, and massively parallel data collection.These advantages avoid considerable investment in robotic hardware.
  • Motivation: Designing individualized tasks, assets, scene configurations, and training supervisions requires substantial human effort, limiting scalable skill learning in simulation.The burden grows across the countless tasks encountered in daily life.
  • Approach: Generative Simulation uses large models to autonomously generate information across the stages required for diverse robotic skill learning.RoboGen is presented as an initial realization of this paradigm.
  • Approach: RoboGen self-proposes skills, constructs conditioned simulation scenes, decomposes tasks, selects learning algorithms, and designs training supervision.The learning options include reinforcement learning, motion planning, and trajectory optimization.
  • Approach: Foundation models provide useful object semantics and affordances, but lack essential understanding of dynamics, actuations, and physical interactions for directly producing robot actions.RoboGen therefore extracts information within the models’ demonstrated capabilities rather than directly yielding policies or low-level actions.
  • Results: RoboGen delivers a continuous stream of diversified demonstrations spanning rigid and articulated manipulation, deformable manipulation, and legged locomotion.The authors report diversity beyond previous human-crafted robotic datasets with minimal human involvement beyond prompt designs and in-context examples.
  • Significance: The pipeline transfers knowledge from large-scale models toward automated large-scale skill training and demonstration collection for generalizable robotic systems.The paper describes this as a step toward building such systems.

2. Related Work

Related work establishes simulation platforms and large-scale benchmarks for robotic learning, while RoboGen extends this direction through generative automation and broader task coverage. Prior simulation datasets are mostly manually built and human-labeled.

  • Robotic skill learning in simulations: Physics-based platforms support rigid-body, deformable-object, and multi-material simulation for accelerating robotics research.These platforms are used across manipulation, flight, and locomotion settings.
  • Scaling up simulation environments: Large-scale simulation benchmarks provide platforms for scalable skill learning and standardized evaluation.Most prior simulation datasets are manually built with human labeling.
  • Foundation and generative models for robotics: Foundation and generative models have been explored for robotics through approaches including code generation.The related-work passage positions RoboGen within this broader use of models across imagery, language, and other modalities.
  • Foundation and generative models for robotics: RoboGen additionally performs scene generation and automatic algorithm selection while covering complex articulated manipulation, locomotion, and soft-body manipulation.This contrasts with the narrower task coverage described for the cited prior line of work.

3. RoboGen

RoboGen is an automated generative-simulation pipeline that proposes tasks, generates scenes and supervision, and learns skills using task-appropriate algorithms. It repeatedly combines foundation-model outputs with simulation to support diverse robotic tasks.

  • Pipeline overview: RoboGen uses foundation models’ common-sense and generative capabilities to automate task, scene, and training-supervision generation.The pipeline targets rigid and articulated manipulation, soft-body manipulation, and legged locomotion.
  • Task Proposal: Task proposal seeds an LLM with a robot type and sampled object, then generates meaningful and diverse skills for the robot to learn.Repeated queries with different objects and examples support varied tasks based on object affordances when needed.
  • Pipeline overview: The pipeline comprises task proposal, scene generation, training-supervision generation, and skill learning with generated information.These stages are summarized in Figure 2.
  • Task Proposal: GPT-4 task proposals include a name, natural-language description, additional required objects, and relevant joints and links.For example, a microwave task can specify placing soup inside, closing the door, and setting the timer.
  • Scene Generation: Scene generation determines relevant assets, asset sizes, initial asset configurations, and initial scene configuration.The system queries GPT-4 for semantically relevant objects and task-compatible spatial arrangements.
  • Scene Generation: For soft-body tasks, RoboGen uses text-to-image followed by image-to-mesh generation when controllable target shapes matter more than fine geometric and textural detail.The passage contrasts this process with retrieved objects used for rigid manipulation.
  • Training Supervision Generation: RoboGen decomposes tasks into shorter-horizon sub-tasks, selects reinforcement learning, trajectory optimization, or motion planning, and generates corresponding supervision.RL rewards may use simulator low-level states or earth-mover distance for soft-body target shapes.
  • Skill Learning: Long-horizon tasks are learned sequentially, running each sub-task N = 8 times and passing the highest-reward end state to the next sub-task.The system combines learning techniques selected conditionally on the generated task.

4. Experiments

RoboGen is evaluated as an automated pipeline for generating diverse simulation tasks, valid scenes and training supervisions, then learning robotic skills across manipulation and locomotion settings. Across these evaluations, it produces diverse tasks, achieves an average skill-learning success rate of 0.774 over 69 tasks, and benefits from verification and algorithm selection.

  • Evaluation scope: RoboGen evaluates task diversity, scene validity, training supervision validity, skill-learning performance, and whole-system demonstrations.The experiments use qualitative and quantitative analyses, including diversity metrics, BLIP-2 scores, human verification, success rates, and representative learned skills.
  • Task Diversity: RoboGen achieves higher task diversity than prior manually crafted benchmarks across task semantics and scene images.On 106 generated tasks, RoboGen reports the lowest Self-BLEU and embedding similarity scores, where lower scores indicate better diversity, and supports rigid, articulated, soft-body, and locomotion tasks.
  • Scene Validity: Scene validity improves when RoboGen verifies both object identity and asset size before constructing environments.Removing size verification drastically decreases BLIP-2 scores, while removing object verification lowers scores and increases variance; manual evaluation found 13 scene-generation failures among 155 tasks.
  • Training Supervision Validity: Generated decompositions and reward functions support successful long-horizon skills, but 6 of 155 object-manipulation tasks contain supervision failures.Observed errors include undefined variables, rewards encoding unintended behavior, and difficulty generating correct rewards for continuous motions.
  • Skill Learning: 0.774 average success rate: RoboGen successfully learns skills on 69 benchmarked tasks spanning object manipulation, soft-body manipulation, and locomotion.The reported average indicates roughly three out of four runs successfully learn the skill.
  • Skill Learning: Allowing RoboGen to select among learning algorithms improves articulated-object skill learning compared with using reinforcement learning alone.In a comparison on 12 articulated-object manipulation tasks, skill learning completely fails for most tasks when only RL is used.

5. Conclusion & Limitations

RoboGen automatically proposes and learns diverse robotic skills through a fully generative simulation pipeline that requires minimal human supervision once deployed. The approach can produce many demonstrations, but remains constrained by skill verification and sim-to-real gaps.

  • RoboGen automatically generates diverse tasks, scenes, and training supervisions for scalable robotic skill learning in simulation.
  • Its fully generative pipeline can be queried endlessly to produce skill demonstrations across diverse tasks and environments.
  • Large-scale verification of learned skills remains a challenge in the current pipeline.
  • RoboGen is intrinsically constrained by sim-to-real gaps for real-world deployment.

A.1. Asset Retrieval and Verification

RoboGen retrieves assets using language similarity and generates meshes for soft-body tasks when controllable target shapes matter more than fine geometry or texture.

  • RoboGen retrieves k Objaverse objects whose Sentence-Bert language embeddings most closely match the target asset description.
  • The asset-retrieval process combines default and cleaned Objaverse annotations into language descriptions.
  • For soft-body manipulation, GPT-4 specifies target shapes that are converted from text to image and then image to mesh.

A.2. Collision Resolving in Scene Generation

RoboGen initializes object poses using LLM spatial reasoning, checks the resulting scene for collisions, and resolves detected contacts by separating object centers of mass.

  • The LLM proposes initial object poses by using basic spatial understanding to place objects in different locations.
  • RoboGen checks the initial scene configuration for potential collisions between objects.
  • Detected collisions are resolved by pushing contacting objects’ centers of mass apart along opposite collision-normal directions.

A.3. Skill Learning

RoboGen selects different skill-learning backends for different task types: reinforcement learning, motion planning, and trajectory optimization. GPT-4 task proposals use higher temperature for diversity, while later stages use lower temperatures for robustness.

  • Skill Learning: RoboGen uses SAC reinforcement learning for object manipulation, BIT* motion planning for action primitives, and Adam trajectory optimization for soft-body manipulation.
  • Skill Learning: For locomotion tasks, RoboGen uses the cross entropy method with simulator dynamics and optimized robot joint angles.
  • Motion Planning: BIT* motion planning generates grasping and approaching motions from sampled surface points and aligned gripper poses.
  • Trajectory Optimization: Soft-body trajectory optimization minimizes Earth Mover’s distance between current and target object shapes.
  • Model Querying: GPT-4 uses temperature 0.8–1.0 for diverse task proposals and 0–0.3 for more robust later-stage responses.

B.1. List of Tasks and Statistics

RoboGen generates a broad set of manipulation, locomotion, and soft-body tasks, with rigid and articulated object manipulation forming the current framework’s main diversity. The generated manipulation tasks are generally short-horizon, averaging 3.13 substeps, while examples span household interactions from doors and drawers to appliances and tools.

  • RoboGen generates rigid and articulated manipulation tasks, soft-body manipulation tasks, and legged locomotion tasks.
  • The average number of substeps is 3.13, including tasks such as opening doors, adjusting displays, rinsing plates, folding chairs, loading printers, and preparing coffee.
  • Most generated manipulation tasks are short-horizon and solvable within 4 substeps, while longer tasks require 8 to 10 substeps.
  • The task list includes multi-stage interactions such as rinsing plates, filling kettles, retrieving objects, loading dishwashers, and operating dispensers.

B.2. Skill Learning Success Rate

RoboGen evaluates whether its generated decompositions and training supervisions support skill learning across articulated manipulation, soft-body manipulation, and locomotion tasks. Among 50 articulated manipulation tasks, the average learning success rate is 0.745.

  • Skill-learning success is evaluated by running the pipeline multiple times for generated tasks with correct training supervisions and judging learned-policy videos by a human evaluator.
  • The average skill-learning success rate is 0.745 across 50 articulated object manipulation tasks.
  • The evaluated articulated tasks include operations such as pressing dishwasher buttons, opening dispenser lids, rotating fan rotors, turning faucets, and manipulating laptops and refrigerators.
  • Skill-learning success rates are also benchmarked for 7 soft-body manipulation tasks and 12 locomotion tasks.

B.3. Failure Analysis

Manual inspection found 19 failures among 155 generated tasks, arising from incorrect scene generation or incorrect training supervision. Scene-generation failures were more frequent than reward-generation failures and included unsupported asset functions, joint-state misunderstandings, and unmatched assets.

  • Manual inspection identified 19 failures among 155 generated tasks due to incorrect scene generation or incorrect training supervisions.
  • 13 of the 19 failures were attributed to incorrect scene generation.
  • Scene-generation failures involved unsupported asset functionality, incorrect mappings from joint angles to semantic states, and unavailable asset pairs with precise spatial relationships.
  • Suggested remedies include additional joint-state checks, self-correction of syntax errors, and environment feedback to improve reward alignment.
  • 6 failures were caused by incorrect reward generation, including undefined variables, reversed intended behavior, and difficulty encoding continuous motions.

C. Asset Generation Results

RoboGen’s text-to-image-to-3D asset pipeline generates meshes for deformable-object manipulation tasks from textual object descriptions. The examples include food items, ropes, dough, and dumplings.

  • The text-to-image-to-3D pipeline is primarily used to generate goal meshes for deformable object manipulation tasks.
  • The pipeline generates example image–mesh pairs for bagels, croissants, ropes, cylinder dough, baguettes, mooncakes, dumplings, and meat.

D. Prompts

RoboGen’s example-based prompts seed diverse robotic tasks across non-articulated manipulation and locomotion. The examples specify household object interactions and locomotion skills together with task-specific reward signals and robot-state targets.

  • D.1. Pre-defined tasks for example-based initialization of RoboGen (purely non-articulated object manipulation tasks).: Example-based seeding initializes non-articulated manipulation tasks for RoboGen.The prompt supplies task examples rather than generating these initial tasks from scratch.
  • D.1. Pre-defined tasks for example-based initialization of RoboGen (purely non-articulated object manipulation tasks).: Additional seeded manipulation tasks include placing books, knives, toys, and tableware in household locations.These examples extend the prompt’s task set across shelving, food preparation, disposal, and meal preparation.
  • D.1. Pre-defined tasks for example-based initialization of RoboGen (purely non-articulated object manipulation tasks).: The manipulation examples cover stacking cups or blocks, weighing food, placing household items, and arranging dishes and cutlery.Examples include cups, peppers, apples, blocks, broccoli, mugs, hangers, books, knives, toys, and table settings.
  • D.2. Pre-defined tasks for example-based initialization of RoboGen (locomotion tasks).: For locomotion, RoboGen includes 3 prompt examples as seeds for skill proposal.The locomotion examples include walking to a ball, flipping rightwards, and jumping backward.
  • D.2. Pre-defined tasks for example-based initialization of RoboGen (locomotion tasks).: Locomotion rewards use robot pose, velocity, orientation axes, height, and energy consumption to supervise generated skills.The reward combines velocity, angular velocity, face, up, side, and height terms with a default energy penalty.
  • D.2. Pre-defined tasks for example-based initialization of RoboGen (locomotion tasks).: The backward-jump reward targets velocity [-5.0, 0, 0.0], preserves the initial orientation, and discourages spinning.The target velocity specifies a backward jump, while target up, face, side, and angular values constrain body orientation and rotation.
  • D.2. Pre-defined tasks for example-based initialization of RoboGen (locomotion tasks).: The ball-walking skill adds penalties for ball motion and robot-to-ball distance.The ball is placed at [1, 0, 0], and the reward penalizes deviation from zero ball velocity and distance from the ball.
  • D.2. Pre-defined tasks for example-based initialization of RoboGen (locomotion tasks).: The flip-rightwards skill sets zero velocity and angular-velocity weights while rewarding target orientation, side direction, and initial height.Its reward uses orientation and height terms, adds the angular and side components, and retains the default energy penalty.

D.3. Prompt for soft body manipulation tasks

RoboGen’s soft-body prompt asks for diverse, meaningful tabletop manipulation tasks in household settings. It then represents each task with initial and goal configurations whose language descriptions drive mesh generation and fixed training supervision.

  • D.3. Prompt for soft body manipulation tasks: The soft-body prompt asks RoboGen to generate robot-learning tasks involving soft materials.It specifically targets manipulation of soft materials, including baked and household scenarios.
  • D.3. Prompt for soft body manipulation tasks: It requests 5 distinct tabletop tasks involving soft-body objects in common household scenarios.The prompt first selects a soft object to manipulate and then chooses tools in a subsequent interaction.
  • D.3. Prompt for soft body manipulation tasks: The tasks must be meaningful, household-relevant, and solvable by a robot arm.The prompt excludes uncommon household activities and requires tabletop manipulation settings.
  • D.3. Prompt for soft body manipulation tasks: The prompt excludes tasks involving chemical changes such as boiling water or frying steak.This constrains the generated tasks to physical manipulation rather than cooking transformations.
  • D.3. Prompt for soft body manipulation tasks: RoboGen represents soft-body tasks with start and goal YAML configurations that can import generated meshes into simulation.A language-to-mesh system translates descriptions into meshes, while YAML specifies the simulation scene.
  • D.3. Prompt for soft body manipulation tasks: The example reshapes dough into a pretzel by specifying separate dough and pretzel mesh entries, positions, scales, colors, and prompts.Image prompts feed a text-to-image model, and mesh prompts feed an image-to-mesh model.
  • D.3. Prompt for soft body manipulation tasks: The prompt asks for a named task and guides the model to generate household-relevant soft-object manipulation tasks without chemical changes.The requested output begins with a task name and a list of five task names.

D.4. All prompts for articulated object centric manipulation tasks

RoboGen generates articulated-object manipulation tasks from object articulation trees and semantics, then converts proposals into simulator scenes and training supervision. The prompts connect object affordances, relevant links and joints, scene assets, and YAML representations.

  • D.4. All prompts for articulated object centric manipulation tasks: The articulated-object prompt section documents prompts for task proposal, scene generation, and training supervision.The task-proposal stage is illustrated with articulated household objects such as ovens and trash cans.
  • D.4.1. PROMPT FOR TASK PROPOSAL.: RoboGen samples an object from a predefined pool and asks GPT-4 to propose tasks based on its functionality and affordances.The prompt uses the robot type, articulation tree, and semantic descriptions to condition task generation.
  • D.4.1. PROMPT FOR TASK PROPOSAL.: The proposal format records each task’s name, description, additional objects, required links, and actuated joints.The prompt explains why each link and joint is needed for the task.
  • D.4.1. PROMPT FOR TASK PROPOSAL.: For an oven, generated tasks include opening the door, adjusting temperature, heating a hamburger, and setting the timer.These tasks map semantic links such as the door and knobs to their corresponding joints.
  • D.4.2. SCENE GENERATION PROMPT.: After task proposal, RoboGen asks GPT-4 to generate a YAML scene describing the initial configuration for simulation.The scene-generation prompt includes three input-output examples and is designed for subsequent simulator processing.
  • D.4.2. SCENE GENERATION PROMPT.: The scene prompt specifies a mobile Franka Panda mounted at (1, 1, 0), with object placement determined by whether a table is used.It defines table placement at (0, 0, 0) with height 0.6m when required.
  • D.4.2. SCENE GENERATION PROMPT.: Each scene object is described by type, name, size in meters, and a concise language description of its mesh.The object fields are intended to make assets referable and physically scaled in the simulator.
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