Video2Policy: Scaling up Manipulation Tasks in Simulation through Internet Videos

• URL: http://arxiv.org/abs/2502.09886v1
• Date: Fri, 14 Feb 2025 03:22:03 GMT
• Title: Video2Policy: Scaling up Manipulation Tasks in Simulation through Internet Videos
• Authors: Weirui Ye, Fangchen Liu, Zheng Ding, Yang Gao, Oleh Rybkin, Pieter Abbeel,
• Abstract summary: We introduce Video2Policy, a novel framework that leverages internet RGB videos to reconstruct tasks based on everyday human behavior. Our method can successfully train RL policies on such tasks, including complex and challenging tasks such as throwing. We show that the generated simulation data can be scaled up for training a general policy, and it can be transferred back to the real robot in a Real2Sim2Real way.
• Score: 61.925837909969815
• License:
• Abstract: Simulation offers a promising approach for cheaply scaling training data for generalist policies. To scalably generate data from diverse and realistic tasks, existing algorithms either rely on large language models (LLMs) that may hallucinate tasks not interesting for robotics; or digital twins, which require careful real-to-sim alignment and are hard to scale. To address these challenges, we introduce Video2Policy, a novel framework that leverages internet RGB videos to reconstruct tasks based on everyday human behavior. Our approach comprises two phases: (1) task generation in simulation from videos; and (2) reinforcement learning utilizing in-context LLM-generated reward functions iteratively. We demonstrate the efficacy of Video2Policy by reconstructing over 100 videos from the Something-Something-v2 (SSv2) dataset, which depicts diverse and complex human behaviors on 9 different tasks. Our method can successfully train RL policies on such tasks, including complex and challenging tasks such as throwing. Finally, we show that the generated simulation data can be scaled up for training a general policy, and it can be transferred back to the real robot in a Real2Sim2Real way.

Related papers

• Robot Learning with Super-Linear Scaling [20.730206708381704]
  CASHER is a pipeline for scaling up data collection and learning in simulation where the performance scales superlinearly with human effort. We show that CASHER enables fine-tuning of pre-trained policies to a target scenario using a video scan without any additional human effort.
  arXiv  Detail & Related papers  (2024-12-02T18:12:02Z)
• GenSim2: Scaling Robot Data Generation with Multi-modal and Reasoning LLMs [38.281562732050084]
  GenSim2 is a scalable framework for complex and realistic simulation task creation. The pipeline can generate data for up to 100 articulated tasks with 200 objects and reduce the required human efforts. We show a promising usage of GenSim2 that the generated data can be used for zero-shot transfer or co-train with real-world collected data.
  arXiv  Detail & Related papers  (2024-10-04T17:51:33Z)
• TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correction [25.36756787147331]
  Learning in simulation and transferring the learned policy to the real world has the potential to enable generalist robots. We propose a data-driven approach to enable successful sim-to-real transfer based on a human-in-the-loop framework. We show that our approach can achieve successful sim-to-real transfer in complex and contact-rich manipulation tasks such as furniture assembly.
  arXiv  Detail & Related papers  (2024-05-16T17:59:07Z)
• Learning Interactive Real-World Simulators [96.5991333400566]
  We explore the possibility of learning a universal simulator of real-world interaction through generative modeling. We use the simulator to train both high-level vision-language policies and low-level reinforcement learning policies. Video captioning models can benefit from training with simulated experience, opening up even wider applications.
  arXiv  Detail & Related papers  (2023-10-09T19:42:22Z)
• Hindsight States: Blending Sim and Real Task Elements for Efficient Reinforcement Learning [61.3506230781327]
  In robotics, one approach to generate training data builds on simulations based on dynamics models derived from first principles. Here, we leverage the imbalance in complexity of the dynamics to learn more sample-efficiently. We validate our method on several challenging simulated tasks and demonstrate that it improves learning both alone and when combined with an existing hindsight algorithm.
  arXiv  Detail & Related papers  (2023-03-03T21:55:04Z)
• Sim2real Transfer Learning for Point Cloud Segmentation: An Industrial Application Case on Autonomous Disassembly [55.41644538483948]
  We present an industrial application case that uses sim2real transfer learning for point cloud data. We provide insights on how to generate and process synthetic point cloud data. A novel patch-based attention network is proposed additionally to tackle this problem.
  arXiv  Detail & Related papers  (2023-01-12T14:00:37Z)
• DeXtreme: Transfer of Agile In-hand Manipulation from Simulation to Reality [64.51295032956118]
  We train a policy that can perform robust dexterous manipulation on an anthropomorphic robot hand. Our work reaffirms the possibilities of sim-to-real transfer for dexterous manipulation in diverse kinds of hardware and simulator setups.
  arXiv  Detail & Related papers  (2022-10-25T01:51:36Z)
• Reactive Long Horizon Task Execution via Visual Skill and Precondition Models [59.76233967614774]
  We describe an approach for sim-to-real training that can accomplish unseen robotic tasks using models learned in simulation to ground components of a simple task planner. We show an increase in success rate from 91.6% to 98% in simulation and from 10% to 80% success rate in the real-world as compared with naive baselines.
  arXiv  Detail & Related papers  (2020-11-17T15:24:01Z)

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.