SPINE: Online Semantic Planning for Missions with Incomplete Natural Language Specifications in Unstructured Environments

• URL: http://arxiv.org/abs/2410.03035v3
• Date: Fri, 21 Mar 2025 01:34:48 GMT
• Title: SPINE: Online Semantic Planning for Missions with Incomplete Natural Language Specifications in Unstructured Environments
• Authors: Zachary Ravichandran, Varun Murali, Mariliza Tzes, George J. Pappas, Vijay Kumar,
• Abstract summary: Large Language Models (LLMs) have demonstrated powerful contextual reasoning abilities over a range of robotic tasks.<n>SPINE is an online planner for missions with incomplete mission specifications provided in natural language.<n>We evaluate SPINE in simulation and real-world settings with missions that require multiple steps of semantic reasoning and exploration.
• Score: 27.485636944766718
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As robots become increasingly capable, users will want to describe high-level missions and have robots infer the relevant details. Because pre-built maps are difficult to obtain in many realistic settings, accomplishing such missions will require the robot to map and plan online. While many semantic planning methods operate online, they are typically designed for well specified missions such as object search or exploration. Recently, Large Language Models (LLMs) have demonstrated powerful contextual reasoning abilities over a range of robotic tasks described in natural language. However, existing LLM-enabled planners typically do not consider online planning or complex missions; rather, relevant subtasks and semantics are provided by a pre-built map or a user. We address these limitations via SPINE, an online planner for missions with incomplete mission specifications provided in natural language. The planner uses an LLM to reason about subtasks implied by the mission specification and then realizes these subtasks in a receding horizon framework. Tasks are automatically validated for safety and refined online with new map observations. We evaluate SPINE in simulation and real-world settings with missions that require multiple steps of semantic reasoning and exploration in cluttered outdoor environments of over 20,000m$^2$. Compared to baselines that use existing LLM-enabled planning approaches, our method is over twice as efficient in terms of time and distance, requires less user interactions, and does not require a full map. Additional resources are provided at https://zacravichandran.github.io/SPINE.

Related papers

• Leveraging LLMs for Mission Planning in Precision Agriculture [3.3088495893219885]
  We present an end-to-end system that enables users to assign complex data collection tasks to autonomous robots using natural language instructions.<n>To enhance reusability, mission plans are encoded using an existing IEEE task specification standard, and are executed on robots via ROS2 nodes that bridge high-level mission descriptions with existing ROS libraries.
  arXiv  Detail & Related papers  (2025-06-11T18:25:23Z)
• Deploying Foundation Model-Enabled Air and Ground Robots in the Field: Challenges and Opportunities [65.98704516122228]
  The integration of foundation models (FMs) into robotics has enabled robots to understand natural language and reason about the semantics in their environments.<n>This paper addresses the deployment of FM-enabled robots in the field, where missions often require a robot to operate in large-scale and unstructured environments.<n>We present the first demonstration of large-scale LLM-enabled robot planning in unstructured environments with several kilometers of missions.
  arXiv  Detail & Related papers  (2025-05-14T15:28:43Z)
• Air-Ground Collaboration for Language-Specified Missions in Unknown Environments [62.56917065429864]
  We present a first-of-its-kind system where an unmanned aerial vehicle (UAV) and an unmanned ground vehicle (UGV) are able to collaboratively accomplish missions specified in natural language.<n>We leverage a Large Language Model (LLM)-enabled planner to reason over semantic-metric maps that are built online and opportunistically shared between an aerial and a ground robot.
  arXiv  Detail & Related papers  (2025-05-14T03:33:46Z)
• LLM+MAP: Bimanual Robot Task Planning using Large Language Models and Planning Domain Definition Language [17.914580097058106]
  Bimanual robotic manipulation presents an inherent challenge due to the complexity involved in the spatial and temporal coordination between two hands. Existing works predominantly focus on attaining human-level manipulation skills for robotic hands, yet little attention has been paid to task planning on long-horizon timescales. This paper introduces LLM+MAP, a bimanual planning framework that integrates LLM reasoning and multi-agent planning.
  arXiv  Detail & Related papers  (2025-03-21T17:04:01Z)
• Simultaneous Localization and Affordance Prediction for Tasks in Egocentric Video [18.14234312389889]
  We present a system which trains on spatially-localized egocentric videos in order to connect visual input and task descriptions. We show our approach outperforms the baseline of using a VLM to map similarity of a task's description over a set of location-tagged images. The resulting system enables robots to use egocentric sensing to navigate to physical locations of novel tasks specified in natural language.
  arXiv  Detail & Related papers  (2024-07-18T18:55:56Z)
• 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)
• TravelPlanner: A Benchmark for Real-World Planning with Language Agents [63.199454024966506]
  We propose TravelPlanner, a new planning benchmark that focuses on travel planning, a common real-world planning scenario. It provides a rich sandbox environment, various tools for accessing nearly four million data records, and 1,225 meticulously curated planning intents and reference plans. Comprehensive evaluations show that the current language agents are not yet capable of handling such complex planning tasks-even GPT-4 only achieves a success rate of 0.6%.
  arXiv  Detail & Related papers  (2024-02-02T18:39:51Z)
• 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)
• Navigation with Large Language Models: Semantic Guesswork as a Heuristic for Planning [73.0990339667978]
  Navigation in unfamiliar environments presents a major challenge for robots. We use language models to bias exploration of novel real-world environments. We evaluate LFG in challenging real-world environments and simulated benchmarks.
  arXiv  Detail & Related papers  (2023-10-16T06:21:06Z)
• Embodied Task Planning with Large Language Models [86.63533340293361]
  We propose a TAsk Planing Agent (TaPA) in embodied tasks for grounded planning with physical scene constraint. During inference, we discover the objects in the scene by extending open-vocabulary object detectors to multi-view RGB images collected in different achievable locations. Experimental results show that the generated plan from our TaPA framework can achieve higher success rate than LLaVA and GPT-3.5 by a sizable margin.
  arXiv  Detail & Related papers  (2023-07-04T17:58:25Z)
• AutoTAMP: Autoregressive Task and Motion Planning with LLMs as Translators and Checkers [20.857692296678632]
  For effective human-robot interaction, robots need to understand, plan, and execute complex, long-horizon tasks. Recent advances in large language models have shown promise for translating natural language into robot action sequences. We show that our approach outperforms several methods using LLMs as planners in complex task domains.
  arXiv  Detail & Related papers  (2023-06-10T21:58:29Z)
• ProgPrompt: Generating Situated Robot Task Plans using Large Language Models [68.57918965060787]
  Large language models (LLMs) can be used to score potential next actions during task planning. We present a programmatic LLM prompt structure that enables plan generation functional across situated environments.
  arXiv  Detail & Related papers  (2022-09-22T20:29:49Z)
• Inner Monologue: Embodied Reasoning through Planning with Language Models [81.07216635735571]
  Large Language Models (LLMs) can be applied to domains beyond natural language processing. LLMs planning in embodied environments need to consider not just what skills to do, but also how and when to do them. We propose that by leveraging environment feedback, LLMs are able to form an inner monologue that allows them to more richly process and plan in robotic control scenarios.
  arXiv  Detail & Related papers  (2022-07-12T15:20:48Z)
• Can Foundation Models Perform Zero-Shot Task Specification For Robot Manipulation? [54.442692221567796]
  Task specification is critical for engagement of non-expert end-users and adoption of personalized robots. A widely studied approach to task specification is through goals, using either compact state vectors or goal images from the same robot scene. In this work, we explore alternate and more general forms of goal specification that are expected to be easier for humans to specify and use.
  arXiv  Detail & Related papers  (2022-04-23T19:39:49Z)
• Learning Language-Conditioned Robot Behavior from Offline Data and Crowd-Sourced Annotation [80.29069988090912]
  We study the problem of learning a range of vision-based manipulation tasks from a large offline dataset of robot interaction. We propose to leverage offline robot datasets with crowd-sourced natural language labels. We find that our approach outperforms both goal-image specifications and language conditioned imitation techniques by more than 25%.
  arXiv  Detail & Related papers  (2021-09-02T17:42:13Z)

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.