Large Language Models for Orchestrating Bimanual Robots

• URL: http://arxiv.org/abs/2404.02018v2
• Date: Thu, 10 Oct 2024 15:07:42 GMT
• Title: Large Language Models for Orchestrating Bimanual Robots
• Authors: Kun Chu, Xufeng Zhao, Cornelius Weber, Mengdi Li, Wenhao Lu, Stefan Wermter,
• Abstract summary: We present LAnguage-model-based Bimanual ORchestration (LABOR) to analyze task configurations and devise coordination control policies. We evaluate our method through simulated experiments involving two classes of long-horizon tasks using the NICOL humanoid robot.
• Score: 19.60907949776435
• License:
• Abstract: Although there has been rapid progress in endowing robots with the ability to solve complex manipulation tasks, generating control policies for bimanual robots to solve tasks involving two hands is still challenging because of the difficulties in effective temporal and spatial coordination. With emergent abilities in terms of step-by-step reasoning and in-context learning, Large Language Models (LLMs) have demonstrated promising potential in a variety of robotic tasks. However, the nature of language communication via a single sequence of discrete symbols makes LLM-based coordination in continuous space a particular challenge for bimanual tasks. To tackle this challenge, we present LAnguage-model-based Bimanual ORchestration (LABOR), an agent utilizing an LLM to analyze task configurations and devise coordination control policies for addressing long-horizon bimanual tasks. We evaluate our method through simulated experiments involving two classes of long-horizon tasks using the NICOL humanoid robot. Our results demonstrate that our method outperforms the baseline in terms of success rate. Additionally, we thoroughly analyze failure cases, offering insights into LLM-based approaches in bimanual robotic control and revealing future research trends. The project website can be found at http://labor-agent.github.io.

Related papers

• CurricuLLM: Automatic Task Curricula Design for Learning Complex Robot Skills using Large Language Models [19.73329768987112]
  CurricuLLM is a curriculum learning tool for complex robot control tasks. It generates subtasks that aid target task learning in natural language form. It also translates natural language description of subtasks into executable code. CurricuLLM can aid learning complex robot control tasks.
  arXiv  Detail & Related papers  (2024-09-27T01:48:16Z)
• COHERENT: Collaboration of Heterogeneous Multi-Robot System with Large Language Models [49.24666980374751]
  COHERENT is a novel LLM-based task planning framework for collaboration of heterogeneous multi-robot systems. A Proposal-Execution-Feedback-Adjustment mechanism is designed to decompose and assign actions for individual robots. The experimental results show that our work surpasses the previous methods by a large margin in terms of success rate and execution efficiency.
  arXiv  Detail & Related papers  (2024-09-23T15:53:41Z)
• Plan-Seq-Learn: Language Model Guided RL for Solving Long Horizon Robotics Tasks [50.27313829438866]
  Plan-Seq-Learn (PSL) is a modular approach that uses motion planning to bridge the gap between abstract language and learned low-level control. PSL achieves success rates of over 85%, out-performing language-based, classical, and end-to-end approaches.
  arXiv  Detail & Related papers  (2024-05-02T17:59:31Z)
• Interactive Planning Using Large Language Models for Partially Observable Robotics Tasks [54.60571399091711]
  Large Language Models (LLMs) have achieved impressive results in creating robotic agents for performing open vocabulary tasks. We present an interactive planning technique for partially observable tasks using LLMs.
  arXiv  Detail & Related papers  (2023-12-11T22:54:44Z)
• LoHoRavens: A Long-Horizon Language-Conditioned Benchmark for Robotic Tabletop Manipulation [38.66406497318709]
  This work focuses on the tabletop manipulation task and releases a simulation benchmark, textitLoHoRavens, which covers various long-horizon reasoning aspects spanning color, size, space, arithmetics and reference. We investigate two methods of bridging the modality gap: caption generation and learnable interface for incorporating explicit and implicit observation feedback to the LLM.
  arXiv  Detail & Related papers  (2023-10-18T14:53:14Z)
• LEMMA: Learning Language-Conditioned Multi-Robot Manipulation [21.75163634731677]
  LanguagE-Conditioned Multi-robot MAnipulation (LEMMA) LeMMA features 8 types of procedurally generated tasks with varying degree of complexity. For each task, we provide 800 expert demonstrations and human instructions for training and evaluations.
  arXiv  Detail & Related papers  (2023-08-02T04:37:07Z)
• Leveraging Sequentiality in Reinforcement Learning from a Single Demonstration [68.94506047556412]
  We propose to leverage a sequential bias to learn control policies for complex robotic tasks using a single demonstration. We show that DCIL-II can solve with unprecedented sample efficiency some challenging simulated tasks such as humanoid locomotion and stand-up.
  arXiv  Detail & Related papers  (2022-11-09T10:28:40Z)
• Learning Neuro-Symbolic Skills for Bilevel Planning [63.388694268198655]
  Decision-making is challenging in robotics environments with continuous object-centric states, continuous actions, long horizons, and sparse feedback. Hierarchical approaches, such as task and motion planning (TAMP), address these challenges by decomposing decision-making into two or more levels of abstraction. Our main contribution is a method for learning parameterized polices in combination with operators and samplers.
  arXiv  Detail & Related papers  (2022-06-21T19:01:19Z)
• 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)
• Bottom-Up Skill Discovery from Unsegmented Demonstrations for Long-Horizon Robot Manipulation [55.31301153979621]
  We tackle real-world long-horizon robot manipulation tasks through skill discovery. We present a bottom-up approach to learning a library of reusable skills from unsegmented demonstrations. Our method has shown superior performance over state-of-the-art imitation learning methods in multi-stage manipulation tasks.
  arXiv  Detail & Related papers  (2021-09-28T16:18:54Z)

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.