Twisting Lids Off with Two Hands

• URL: http://arxiv.org/abs/2403.02338v2
• Date: Mon, 14 Oct 2024 06:02:45 GMT
• Title: Twisting Lids Off with Two Hands
• Authors: Toru Lin, Zhao-Heng Yin, Haozhi Qi, Pieter Abbeel, Jitendra Malik,
• Abstract summary: We show how policies trained in simulation can be effectively and efficiently transferred to the real world. Specifically, we consider the problem of twisting lids of various bottle-like objects with two hands. This is the first sim-to-real RL system that enables such capabilities on bimanual multi-fingered hands.
• Score: 82.21668778600414
• License:
• Abstract: Manipulating objects with two multi-fingered hands has been a long-standing challenge in robotics, due to the contact-rich nature of many manipulation tasks and the complexity inherent in coordinating a high-dimensional bimanual system. In this work, we share novel insights into physical modeling, real-time perception, and reward design that enable policies trained in simulation using deep reinforcement learning (RL) to be effectively and efficiently transferred to the real world. Specifically, we consider the problem of twisting lids of various bottle-like objects with two hands, demonstrating policies with generalization capabilities across a diverse set of unseen objects as well as dynamic and dexterous behaviors. To the best of our knowledge, this is the first sim-to-real RL system that enables such capabilities on bimanual multi-fingered hands.

Related papers

• Learning the Generalizable Manipulation Skills on Soft-body Tasks via Guided Self-attention Behavior Cloning Policy [9.345203561496552]
  GP2E behavior cloning policy can guide the agent to learn the generalizable manipulation skills from soft-body tasks. Our findings highlight the potential of our method to improve the generalization abilities of Embodied AI models.
  arXiv  Detail & Related papers  (2024-10-08T07:31:10Z)
• Learning Visuotactile Skills with Two Multifingered Hands [80.99370364907278]
  We explore learning from human demonstrations using a bimanual system with multifingered hands and visuotactile data. Our results mark a promising step forward in bimanual multifingered manipulation from visuotactile data.
  arXiv  Detail & Related papers  (2024-04-25T17:59:41Z)
• Bi-Manual Block Assembly via Sim-to-Real Reinforcement Learning [24.223788665601678]
  Two xArm6 robots solve the U-shape assembly task with a success rate of above90% in simulation, and 50% on real hardware without any additional real-world fine-tuning. Our results present a significant step forward for bi-arm capability on real hardware, and we hope our system can inspire future research on deep RL and Sim2Real transfer bi-manualpolicies.
  arXiv  Detail & Related papers  (2023-03-27T01:25:24Z)
• Dexterous Manipulation from Images: Autonomous Real-World RL via Substep Guidance [71.36749876465618]
  We describe a system for vision-based dexterous manipulation that provides a "programming-free" approach for users to define new tasks. Our system includes a framework for users to define a final task and intermediate sub-tasks with image examples. experimental results with a four-finger robotic hand learning multi-stage object manipulation tasks directly in the real world.
  arXiv  Detail & Related papers  (2022-12-19T22:50:40Z)
• 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)
• ASE: Large-Scale Reusable Adversarial Skill Embeddings for Physically Simulated Characters [123.88692739360457]
  General-purpose motor skills enable humans to perform complex tasks. These skills also provide powerful priors for guiding their behaviors when learning new tasks. We present a framework for learning versatile and reusable skill embeddings for physically simulated characters.
  arXiv  Detail & Related papers  (2022-05-04T06:13:28Z)
• Bi-Manual Manipulation and Attachment via Sim-to-Real Reinforcement Learning [23.164743388342803]
  We study how to solve bi-manual tasks using reinforcement learning trained in simulation. We also discuss modifications to our simulated environment which lead to effective training of RL policies. In this work, we design a Connect Task, where the aim is for two robot arms to pick up and attach two blocks with magnetic connection points.
  arXiv  Detail & Related papers  (2022-03-15T21:49:20Z)
• COCOI: Contact-aware Online Context Inference for Generalizable Non-planar Pushing [87.7257446869134]
  General contact-rich manipulation problems are long-standing challenges in robotics. Deep reinforcement learning has shown great potential in solving robot manipulation tasks. We propose COCOI, a deep RL method that encodes a context embedding of dynamics properties online.
  arXiv  Detail & Related papers  (2020-11-23T08:20:21Z)

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.