DexMimicGen: Automated Data Generation for Bimanual Dexterous Manipulation via Imitation Learning
- URL: http://arxiv.org/abs/2410.24185v1
- Date: Thu, 31 Oct 2024 17:48:45 GMT
- Title: DexMimicGen: Automated Data Generation for Bimanual Dexterous Manipulation via Imitation Learning
- Authors: Zhenyu Jiang, Yuqi Xie, Kevin Lin, Zhenjia Xu, Weikang Wan, Ajay Mandlekar, Linxi Fan, Yuke Zhu,
- Abstract summary: We present a large-scale automated data generation system that synthesizes trajectories from human demonstrations for humanoid robots with dexterous hands.
We generate 21K demos across these tasks from just 60 source human demos.
We also present a real-to-sim-to-real pipeline and deploy it on a real-world humanoid can sorting task.
- Score: 42.88605563822155
- License:
- 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. Videos and more are at https://dexmimicgen.github.io/
Related papers
- One-Shot Imitation under Mismatched Execution [7.060120660671016]
Human demonstrations as prompts are a powerful way to program robots to do long-horizon manipulation tasks.
translating these demonstrations into robot-executable actions presents significant challenges due to execution mismatches in movement styles and physical capabilities.
We propose RHyME, a novel framework that automatically aligns human and robot task executions using optimal transport costs.
arXiv Detail & Related papers (2024-09-10T16:11:57Z) - Human-Agent Joint Learning for Efficient Robot Manipulation Skill Acquisition [48.65867987106428]
We introduce a novel system for joint learning between human operators and robots.
It enables human operators to share control of a robot end-effector with a learned assistive agent.
It reduces the need for human adaptation while ensuring the collected data is of sufficient quality for downstream tasks.
arXiv Detail & Related papers (2024-06-29T03:37:29Z) - RealDex: Towards Human-like Grasping for Robotic Dexterous Hand [64.47045863999061]
We introduce RealDex, a pioneering dataset capturing authentic dexterous hand grasping motions infused with human behavioral patterns.
RealDex holds immense promise in advancing humanoid robot for automated perception, cognition, and manipulation in real-world scenarios.
arXiv Detail & Related papers (2024-02-21T14:59:46Z) - MimicGen: A Data Generation System for Scalable Robot Learning using
Human Demonstrations [55.549956643032836]
MimicGen is a system for automatically synthesizing large-scale, rich datasets from only a small number of human demonstrations.
We show that robot agents can be effectively trained on this generated dataset by imitation learning to achieve strong performance in long-horizon and high-precision tasks.
arXiv Detail & Related papers (2023-10-26T17:17:31Z) - Scaling Robot Learning with Semantically Imagined Experience [21.361979238427722]
Recent advances in robot learning have shown promise in enabling robots to perform manipulation tasks.
One of the key contributing factors to this progress is the scale of robot data used to train the models.
We propose an alternative route and leverage text-to-image foundation models widely used in computer vision and natural language processing.
arXiv Detail & Related papers (2023-02-22T18:47:51Z) - From One Hand to Multiple Hands: Imitation Learning for Dexterous
Manipulation from Single-Camera Teleoperation [26.738893736520364]
We introduce a novel single-camera teleoperation system to collect the 3D demonstrations efficiently with only an iPad and a computer.
We construct a customized robot hand for each user in the physical simulator, which is a manipulator resembling the same kinematics structure and shape of the operator's hand.
With imitation learning using our data, we show large improvement over baselines with multiple complex manipulation tasks.
arXiv Detail & Related papers (2022-04-26T17:59:51Z) - Where is my hand? Deep hand segmentation for visual self-recognition in
humanoid robots [129.46920552019247]
We propose the use of a Convolution Neural Network (CNN) to segment the robot hand from an image in an egocentric view.
We fine-tuned the Mask-RCNN network for the specific task of segmenting the hand of the humanoid robot Vizzy.
arXiv Detail & Related papers (2021-02-09T10:34:32Z) - Learning Predictive Models From Observation and Interaction [137.77887825854768]
Learning predictive models from interaction with the world allows an agent, such as a robot, to learn about how the world works.
However, learning a model that captures the dynamics of complex skills represents a major challenge.
We propose a method to augment the training set with observational data of other agents, such as humans.
arXiv Detail & Related papers (2019-12-30T01:10:41Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.