Data-Agnostic Robotic Long-Horizon Manipulation with Vision-Language-Guided Closed-Loop Feedback
Recent advances in language-conditioned robotic manipulation have leveraged imitation and reinforcement learning to enable robots to execute tasks from human commands. However, these methods often suffer from limited generalization, adaptability, and the lack of large-scale specialized datasets, unlike data-rich domains such as computer vision, making long-horizon task execution challenging. To address these gaps, we introduce DAHLIA, a data-agnostic framework for language-conditioned long-horizon robotic manipulation, leveraging large language models (LLMs) for real-time task planning and execution. DAHLIA employs a dual-tunnel architecture, where an LLM-powered planner collaborates with co-planners to decompose tasks and generate executable plans, while a reporter LLM provides closed-loop feedback, enabling adaptive re-planning and ensuring task recovery from potential failures. Moreover, DAHLIA integrates chain-of-thought (CoT) in task reasoning and temporal abstraction for efficient action execution, enhancing traceability and robustness. Our framework demonstrates state-of-the-art performance across diverse long-horizon tasks, achieving strong generalization in both simulated and real-world scenarios. Videos and code are available at https://ghiara.github.io/DAHLIA/.
Code (1)
Tasks
Task PlanningSimilar Papers 제목 키워드 기반
Long-VLA: Unleashing Long-Horizon Capability of Vision Language Action Model for Robot Manipulation
Vision-Language-Action (VLA) models have become a cornerstone in robotic policy learning, leveraging large-scale multimodal data for robust and scalable control. However, existing VLA frameworks primarily address short-h…
Robot ManipulationEfficient Learning of High Level Plans from Play
Real-world robotic manipulation tasks remain an elusive challenge, since they involve both fine-grained environment interaction, as well as the ability to plan for long-horizon goals. Although deep reinforcement learning…
Deep Reinforcement LearningMotion PlanningReinforcement Learning (RL)Vocal Bursts Intensity PredictionIntrinsic Language-Guided Exploration for Complex Long-Horizon Robotic Manipulation Tasks
Current reinforcement learning algorithms struggle in sparse and complex environments, most notably in long-horizon manipulation tasks entailing a plethora of different sequences. In this work, we propose the Intrinsical…
Decision Makingreinforcement-learningReinforcement LearningBiCoord: A Bimanual Manipulation Benchmark towards Long-Horizon Spatial-Temporal Coordination
Bimanual manipulation, i.e., the coordinated use of two robotic arms to complete tasks, is essential for achieving human-level dexterity in robotics. Recent simulation benchmarks, e.g., RoboTwin and RLBench2, have advanc…
Generalizable Long-Horizon Manipulations with Large Language Models
This work introduces a framework harnessing the capabilities of Large Language Models (LLMs) to generate primitive task conditions for generalizable long-horizon manipulations with novel objects and unseen tasks. These t…