RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation (2311.01455v3)

Abstract: 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.

RoboGen: Automating Robotic Skill Acquisition via Generative Simulation

The research paper introduces RoboGen, a generative robotic agent designed to enhance the acquisition of diverse robotic skills by leveraging the capabilities of generative simulations. This approach incorporates recent advancements in foundation and generative models, applying them to automate the generation of diverse tasks, environments, and training supervisions, thus minimizing the need for human input in robot skill learning. The methodology is characterized by a self-directed cycle encompassing proposing new skills, generating corresponding simulation environments, and learning these skills through decomposed sub-tasks, optimal learning strategies, and generated training supervisions.

Key Methodological Contributions

RoboGen's approach consists of several integral stages:

1. Task Proposal: Instead of utilizing LLMs like GPT-4 directly for proposing tasks, RoboGen initializes task generation using specific robot types and objects randomly sampled from a predefined pool. This is followed by employing an LLM to propose meaningful and diverse high-level tasks by leveraging the affordances and semantic understanding of these objects, thus seeding a wide array of skill acquisition scenarios.
2. Scene Generation: Once tasks are proposed, RoboGen generates corresponding simulation scenes, including scene components such as asset queries, their physical parameters, and spatial configurations. It retrieves these assets from large databases like Objaverse or generates them using text-to-image-to-3D mesh models when not available, ensuring that real-world scales and plausible configurations are maintained.
3. Training Supervision Generation: RoboGen also automates the generation of training supervisions, which include decomposing tasks into sub-tasks and selecting suitable learning algorithms among reinforcement learning, motion planning, and trajectory optimization for each sub-task. This stage ensures that the skills are learnable within the given simulation constraints.
4. Skill Learning: With the automated frameworks set in place, RoboGen integrates various learning algorithms tailored to the type of task, whether it involves rigid object manipulation, locomotion, or soft body manipulation, thereby optimizing for task completion through sequential sub-task execution.

Experimental Evaluation and Insights

The experiments conducted using RoboGen within the Genesis simulation platform resulted in notable outcomes:

• Diversity and Complexity: RoboGen demonstrated a higher diversity in task generation compared to traditional benchmarks like RLBench, MetaWorld, and Maniskill2, as quantified by metrics such as Self-BLEU and embedding similarity scores.
• Scene and Supervision Validity: The effectiveness of RoboGen’s generative pipelines was validated through visual and numeric assessments, ensuring that generated tasks and scenes were feasible and practically executable by the robotic systems.
• Skill Acquisition Success: The inclusion of multiple learning strategies within RoboGen was empirically shown to enhance the learning success rates for complex tasks compared to approaches relying solely on reinforcement learning.

Implications and Potential Developments

The introduction of RoboGen posits several implications for both practical robotics and theoretical advancements. By reducing the dependency on human intervention for designing and supervising robot training, RoboGen can potentially lead to faster and more scalable development of robotic skills applicable in real-world scenarios beyond controlled environments. The system’s interoperability with existing generative models allows it to be upgraded with new capabilities as these models evolve, ensuring continued improvement and adaptation.

Further developments might include enhanced integration of multi-modal models to verify and refine learned skills and the transference of these skills into real-world settings, thereby narrowing the sim-to-real gap. As AI and robotics converge in developing more sophisticated autonomous agents, RoboGen serves as an instrumental step towards realizing robots that learn and operate with a greater degree of autonomy and adaptability.