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DexMimicGen: Automated Data Generation for Bimanual Dexterous Manipulation via Imitation Learning (2410.24185v2)

Published 31 Oct 2024 in cs.RO, cs.AI, cs.CV, and cs.LG

Abstract: Imitation learning from human demonstrations is an effective means to teach robots manipulation skills. But data acquisition is a major bottleneck in applying this paradigm more broadly, due to the amount of cost and human effort involved. There has been significant interest in imitation learning for bimanual dexterous robots, like humanoids. Unfortunately, data collection is even more challenging here due to the challenges of simultaneously controlling multiple arms and multi-fingered hands. Automated data generation in simulation is a compelling, scalable alternative to fuel this need for data. To this end, we introduce DexMimicGen, a large-scale automated data generation system that synthesizes trajectories from a handful of human demonstrations for humanoid robots with dexterous hands. We present a collection of simulation environments in the setting of bimanual dexterous manipulation, spanning a range of manipulation behaviors and different requirements for coordination among the two arms. We generate 21K demos across these tasks from just 60 source human demos and study the effect of several data generation and policy learning decisions on agent performance. Finally, we present a real-to-sim-to-real pipeline and deploy it on a real-world humanoid can sorting task. Generated datasets, simulation environments and additional results are at https://dexmimicgen.github.io/

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Summary

  • The paper introduces an automated system that generates 21,000 demos from only 60 human demonstrations for bimanual tasks.
  • It employs a multi-step simulation environment and a real-to-sim-to-real pipeline to achieve a 90% success rate in humanoid tasks.
  • The method significantly reduces human effort and cost in training dexterous robots, offering a scalable framework for imitation learning research.

An Overview of DexMimicGen: Automated Data Generation for Bimanual Dexterous Manipulation

The paper "DexMimicGen: Automated Data Generation for Bimanual Dexterous Manipulation via Imitation Learning" presents a novel method for generating large-scale training datasets in the domain of robotic manipulation. The primary issue addressed is the high cost and human effort associated with acquiring demonstration data necessary for training bimanual dexterous robots. By automating the data generation process via simulation, DexMimicGen aims to mitigate these constraints.

Key Contributions

DexMimicGen introduces a method that synthesizes trajectories for humanoid robots with dexterous hands from a limited set of human demonstrations. There are several key contributions made by the authors:

  1. Automated Data Generation System: DexMimicGen generates 21,000 demos across diverse tasks using only 60 source human demonstrations. This data generation system leverages an asynchronous per-arm execution strategy, synchronization, and sequential constraints to enable multi-arm coordination.
  2. Simulation Environment Development: A suite of simulation environments was developed, focusing on tasks that require different coordination behaviors between two arms. The environments facilitate the paper of the effects of data generation and policy learning on agent performance.
  3. Real-to-Sim-to-Real Pipeline: A practical implementation of the developed system was demonstrated by deploying it on a real-world humanoid task (can sorting), achieving a 90% success rate, significantly surpassing baseline performances.

Methodology

DexMimicGen extends upon the principles of MimicGen by enabling data generation for bimanual and dexterous manipulation tasks. The system decomposes tasks into a series of object-centric subtasks categorized into three types: parallel, coordination, and sequential. Each type handles specific challenges such as independent arm actions, synchronization for arm coordination, and enforcing operation order where necessary.

  • Parallel Subtasks: Address independent sub-goals for each arm, executed asynchronously.
  • Coordination Subtasks: Require synchronized execution to maintain relative poses, using either transformation or replay strategies to align actions.
  • Sequential Subtasks: Use ordering constraints to ensure correct task progression between arms.

Implications and Future Directions

The implications of DexMimicGen are significant for both practical applications and theoretical research in robotic manipulation. Practically, the method reduces the need for extensive human data collection, thereby lowering costs and entry barriers for training complex humanoid robots. Theoretically, DexMimicGen provides a framework that could enhance understanding of scalable data-generation techniques and multi-agent coordination.

The DexMimicGen dataset and developed environments also open avenues for future research in imitation learning and robotic control. Evaluating how policy architecture choices affect learning outcomes, as shown in the analysis section, highlights areas for further investigation in optimizing learning strategies. Additionally, real-world trials affirm the potential of transferring simulation-trained models to tangible applications, supporting more nuanced development of robotic capabilities in dynamic settings.

Overall, DexMimicGen delineates a method that not only boosts the efficiency of data acquisition for robotic learning but also provides a robust framework for future advancements in the field of dexterous bimanual manipulation, thereby contributing significantly to both applied and theoretical robotics research.

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