PLAN-SEQ-LEARN: A machine learning method that integrates the long-horizon resolution capabilities of language models with the skill of learned reinforcement learning RL policies

The research field of robotics has changed significantly due to the integration of large language models (LLMs). These advances provide the opportunity to assist robotic systems in solving complex tasks that require intricate planning and manipulation over long periods of time. While robots have traditionally relied on predefined skills and specialized engineering, recent developments show the potential to use LLMs to guide reinforcement learning (RL) policies, bridging the gap between abstract high-level planning and detailed robot control. The challenge remains to translate the sophisticated language processing capabilities of these models into actionable control strategies, particularly in dynamic environments with complex interactions.

Robotic manipulation tasks often require the execution of a range of fine-tuned behaviors, and current robotic systems struggle with the long-term planning required for these tasks due to limitations in low-level control and interaction, particularly in dynamic or high-contact environments. Existing tools such as end-to-end RL or hierarchical methods attempt to bridge the gap between LLMs and robot control, but often suffer from limited adaptability or significant challenges in handling high-contact tasks. The main problem is how to efficiently translate abstract language models into practical robot control, which is traditionally limited by the inability of LLMs to produce low-level control.

The Plan-Seq-Learn (PSL) The framework from researchers at Carnegie Mellon University and Mistral AI is introduced as a modular solution to bridge this gap and integrate LLM-based planning to guide RL policies when solving long-term robotic tasks. PSL decomposes tasks into three phases: high-level language planning (Plan), motion planning (Seq), and RL-based learning (Learn). This allows PSL to handle both non-contact movements and complex interaction strategies. The PSL system uses off-the-shelf vision models to identify the target regions of interest based on high-level language input and provide a structured plan for the sequence of the robot’s actions through motion planning.

PSL uses an LLM to create a high-level plan that sequences robot actions through motion planning. Vision models help predict relevant regions and enable the sequencing module to identify target states for the robot to achieve. The motion planning component controls the robot into these states and the RL policy takes care of performing the required interactions. This modular approach allows RL policies to refine and adjust control strategies based on real-time feedback, allowing a robotic system to handle complex tasks. The research team demonstrated PSL for 25 complex robotics tasks, including high-contact manipulation tasks and long-horizon control tasks, spanning up to 10 stages. These were tasks with up to 10 consecutive stages that required up to 10 separate robot subtasks.

PSL achieved a success rate of over 85%, significantly outperforming existing methods such as SayCan and MoPA-RL. This was particularly evident in high-contact tasks, where PSL’s modular approach allowed the robots to adapt to unexpected conditions in real time and efficiently solve the complex interactions required. The flexibility of the PSL framework enables a modular combination of planning, movement and learning, enabling it to tackle different types of tasks from a wide range of robotics benchmarks. By sharing RL policies across all phases of a task, PSL achieved remarkable efficiency in training speed and task performance, outperforming methods such as E2E and RAPS.

In summary, the research team demonstrated the effectiveness of PSL in leveraging LLMs for high-level planning, sequencing movements using vision models, and refining control strategies through RL. PSL achieves a delicate balance between efficiency and precision in translating abstract language goals into practical robot control. Modular planning and real-time learning make PSL a promising framework for future robotics applications, enabling robots to complete complex tasks with multi-step plans.

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Sana Hassan, Consulting Intern at Marktechpost and dual degree student at IIT Madras, is passionate about using technology and AI to address real-world challenges. With a keen interest in solving practical problems, he brings a new perspective to the interface between AI and real-world solutions.

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