Significant advancements have been made in the field of robotics research with the integration of large language models (LLMs) into robotic systems. This development has enabled robots to better tackle complex tasks that demand detailed planning and sophisticated manipulation, bridging the gap between high-level planning and robotic control. However, challenges persist in transforming the remarkable language processing capabilities of these models into actionable control strategies, particularly when dealing with dynamic environments involving complex interactions.
Historically, robots have encountered difficulties in executing long-horizon planning tasks that require a series of well-honed behaviours. This is due to the constraints in low-level control and interaction capabilities, especially in environments that require extensive contact. Existing methods, like end-to-end reinforcement learning (RL) or hierarchical methods, have tried but failed to effectively link LLMs and robotic control, primarily because of the struggle to adapt or manage tasks involving heavy contact.
A team of researchers from Carnegie Mellon University and Mistral AI has introduced the Plan-Seq-Learn (PSL) framework as a viable solution. This system elegantly integrates LLMs into planning stages to guide RL policies in dealing with long-term robotic tasks. PSL breaks down tasks into three distinct stages – high-level language planning (Plan), motion planning (Seq), and RL-based learning (Learn). As a result, PSL can effectively manage both contact-free motion and complex interaction strategies. It uses standard vision models to identify target areas based on high-level language inputs and creates a structured plan for robot sequences.
PSL utilizes a large language model to create a high-level plan which the robot follows using motion planning. Vision models predict regions of interest, enabling the sequencing module to identify target states for the robot. The robot then moves to these states, with the RL policy undertaking any necessary interactions. Using this modular approach, reinforcement learning policies can adapt and refine control strategies using real-time feedback, thereby enabling robots to complete complicated tasks.
The research team tested PSL on 25 complex robotics tasks, including a diverse range of contact-heavy manipulation tasks and long-horizon control tasks. PSL achieved a success rate of over 85%, significantly outpacing existing methods like SayCan and MoPA-RL. This was particularly apparent in tasks requiring contact, where PSL’s modular approach allowed robots to adapt to unexpected conditions swiftly and complete intricate interactions. By employing a shared RL policy across all stages, PSL achieved impressive efficiency in training speed and task performance, exceeding methods such as E2E and RAPS.
In conclusion, the researchers proved PSL to be effective in employing LLMs for high-level planning, using vision models for sequence motions and achieving optimal control strategies through RL. The balance PSL has achieved in converting abstract language goals into practical robotic control sets it up well for future applications in robotics, given its modular planning approach and capacity for real-time learning.
