RePLan: Robotic Replanning with Perception and Language Models
(2401.04157)Abstract
Advancements in LLMs have demonstrated their potential in facilitating high-level reasoning, logical reasoning and robotics planning. Recently, LLMs have also been able to generate reward functions for low-level robot actions, effectively bridging the interface between high-level planning and low-level robot control. However, the challenge remains that even with syntactically correct plans, robots can still fail to achieve their intended goals due to imperfect plans or unexpected environmental issues. To overcome this, Vision Language Models (VLMs) have shown remarkable success in tasks such as visual question answering. Leveraging the capabilities of VLMs, we present a novel framework called Robotic Replanning with Perception and Language Models (RePLan) that enables online replanning capabilities for long-horizon tasks. This framework utilizes the physical grounding provided by a VLM's understanding of the world's state to adapt robot actions when the initial plan fails to achieve the desired goal. We developed a Reasoning and Control (RC) benchmark with eight long-horizon tasks to test our approach. We find that RePLan enables a robot to successfully adapt to unforeseen obstacles while accomplishing open-ended, long-horizon goals, where baseline models cannot, and can be readily applied to real robots. Find more information at https://replan-lm.github.io/replan.github.io/
Overview
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RePLan is a robotic framework that autonomously generates and adjusts plans through LLMs and Vision Language Models (VLMs) to perform complex tasks.
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RePLan combines high-level contextual understanding and real-time scene interpretation to translate high-level plans into specific actions and adapt to dynamic environments.
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Unlike traditional long-term robotic planning, RePLan doesn't rely on extensive domain knowledge or datasets due to the integration of LLMs for high-level reasoning.
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The system includes two planners; a high-level planner for strategy and a low-level planner for motor actions, with a verifier assessing both to minimize errors.
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In simulated tests, RePLan's agility and adaptability in completing tasks greatly outperformed existing models, increasing success rates significantly.
Overview of the RePLan Framework
RePLan represents an innovative framework that addresses a critical challenge in robotics: enabling robots to perform long-horizon tasks with minimal human intervention. The paper presents a system that can autonomously generate and revise plans for robots by integrating LLMs and Vision Language Models (VLMs). This synergistic approach allows robots to form high-level plans and then translate them into specific low-level actions.
Bridging High-level Planning and Low-level Control
Traditional methods for long-term planning in robotics, such as Hierarchical Reinforcement Learning (HRL) or Imitation Learning (IL), often require expansive domain knowledge and extensive datasets for task learning. By contrast, the use of LLMs offers considerable promise given their capability in high-level reasoning. However, one of the key challenges in the application of LLMs is reconciling their open-ended text generation with the more constrained instructions needed by robots for task execution. Additionally, the task environment is dynamic, and unforeseen changes require robots to adapt quickly. This is where RePLan steps in, combining the high-level contextual understanding of LLMs with real-time scene interpretation from VLMs, thus enabling precise robot task execution and real-time adjustments to the plan.
Integrating Visual Feedback into Replanning
The agility of the RePLan system is in its real-time replanning capabilities. It uses a multi-layered structure with two planners: a high-level planner generates the overarching strategy for the task at hand, while the secondary low-level planner translates these plans into detailed motor actions. Both levels of planning are screened by a verifier to minimize errors. If an initially executed plan does not yield success due to an unexpected incident or environmental change, the robot does not just attempt to repeat the same process. Instead, it calls upon the VLM Perceiver for insights into what went wrong. The Perceiver, trained in tasks such as visual question answering, provides feedback that influences the robot's next course of action.
Testing the Capabilities
The capabilities of RePLan were demonstrated in four different simulated environments, each comprising unique challenges that required a robot to complete multiple steps or adapt to changes. Compared to existing models, RePLan showed a tremendous increase in success rates almost 4 times that of competitive methods in completing a variety of tasks. This underscores its potential to deal effectively with the complexity and variability inherent in real-world robotic applications.
Conclusion
In conclusion, RePLan is a noteworthy step toward true robotic autonomy. With its innovative combination of LLMs and VLMs for planning and execution, it tackles the prevalent problem of rigid task planning that cannot accommodate dynamic environments. Its successful real-time adjustments in response to unforeseen changes mark a shift toward more adaptive, reliable, and intelligent robotic systems. While it's not without limitations, such as a reliance on the accuracies of LLMs and VLMs for interpretation, RePLan presents a fertile ground for further research and development in the field of robotics.