Leveraging Language Models and Motion Planning to Efficiently Learn Long-Horizon Robotic Control Policies

מושגי ליבה

A modular approach that uses language model planning, motion planning, and reinforcement learning to efficiently solve long-horizon robotics tasks from raw visual input.

תקציר

The paper proposes a method called Plan-Seq-Learn (PSL) that integrates the strengths of large language models (LLMs) and reinforcement learning (RL) to solve long-horizon robotics tasks.
The key components of PSL are:
Planning Module:

Uses an LLM to generate a high-level plan for the task, breaking it down into a sequence of target regions and stage termination conditions.
Sequencing Module:

Leverages vision-based pose estimation to determine the target robot pose for each stage of the plan.
Uses motion planning to guide the robot to the target pose.
Learning Module:

Trains an RL policy to learn the low-level control behaviors required to interact with the environment and complete each stage of the plan.
Shares the RL policy across all stages, uses local observations for efficient learning, and introduces a curriculum learning strategy.
The authors demonstrate that PSL outperforms end-to-end RL, hierarchical RL, classical planning, and LLM planning baselines on over 25 challenging robotics tasks with up to 10 stages, achieving success rates over 85%. PSL is particularly effective on contact-rich manipulation tasks that are difficult for prior methods.
The key innovations of PSL are:

Tightly integrating LLM planning, motion planning, and RL to leverage the strengths of each component.
Strategies for efficient RL policy learning from high-level plans, including policy observation space design, shared policy networks, and curricula.
Extensive experimental evaluation showing the effectiveness of PSL on long-horizon robotics tasks.

סטטיסטיקה

"Large Language Models (LLMs) have been shown to be capable of performing high-level planning for long-horizon robotics tasks, yet existing methods require access to a pre-defined skill library."
"End-to-end reinforcement learning (RL) is one paradigm that can produce complex low-level control strategies on robots with minimal assumptions, but is traditionally limited to the short horizon regime due to the significant challenge of exploration in RL."
"PSL achieves state-of-the-art results on over 25 challenging robotics tasks with up to 10 stages. PSL solves long-horizon tasks from raw visual input spanning four benchmarks at success rates of over 85%, out-performing language-based, classical, and end-to-end approaches."

ציטוטים

"Can we instead use the internet-scale knowledge from LLMs for high-level policies, guiding reinforcement learning (RL) policies to efficiently solve robotic control tasks online without requiring a pre-determined set of skills?"
"Our key insight is that LLMs and RL have complementary strengths and weaknesses."
"To our knowledge, ours is the first work enabling language guided RL agents to efficiently learn low-level control strategies for long-horizon robotics tasks."

תובנות מפתח מזוקקות מ:

Plan-Seq-Learn: Language Model Guided RL for Solving Long Horizon Robotics Tasks

by Murtaza Dala... ב- arxiv.org 05-03-2024

https://arxiv.org/pdf/2405.01534.pdf

שאלות מעמיקות

How could the proposed method be extended to enable the agent to refine and expand its own skill repertoire over time, rather than relying on a fixed set of skills?

The proposed method, Plan-Seq-Learn (PSL), could be extended to enable the agent to refine and expand its own skill repertoire over time by incorporating a continual learning framework. Here are some key steps to achieve this extension:

Skill Discovery Mechanism: Implement a mechanism within the agent that allows it to discover new skills autonomously. This could involve exploring the environment, experimenting with different actions, and identifying successful strategies.

Skill Evaluation and Selection: Develop a process for evaluating the effectiveness of newly discovered skills. The agent can assess the utility of each skill based on task performance and reward outcomes.

Skill Retention and Improvement: Once a new skill is identified as valuable, the agent should retain it in its skill repertoire. Additionally, the agent can further refine and improve existing skills through reinforcement learning and practice.

Skill Generalization: Enable the agent to generalize learned skills to new tasks or variations of existing tasks. This can involve transfer learning techniques to apply previously acquired skills to novel scenarios.

Curriculum Learning: Implement a curriculum learning strategy to guide the agent in learning progressively more complex skills. This can help in building a diverse and robust skill set over time.

Meta-Learning: Incorporate meta-learning techniques to facilitate faster adaptation to new tasks and skills. Meta-learning can help the agent quickly learn how to learn, leading to more efficient skill acquisition.

By integrating these elements into the PSL framework, the agent can continuously evolve its skill repertoire, adapt to changing environments, and improve its overall performance over time.

How could the potential challenges and limitations of applying this approach to real-world robotic systems be addressed?

Applying the PSL approach to real-world robotic systems may face several challenges and limitations, which can be addressed through the following strategies:

Hardware Constraints: Real-world robots may have limitations in terms of computational power, sensing capabilities, and actuation precision. To address this, the system can be optimized for efficiency, and hardware upgrades can be considered to meet the requirements of the approach.

Sensory Noise and Uncertainty: Real-world sensors can introduce noise and uncertainty in observations, affecting the performance of the system. Techniques such as sensor fusion, calibration, and filtering can be employed to improve the quality of sensory data.

Safety and Robustness: Ensuring the safety of real-world robotic systems is crucial. Robust control strategies, error handling mechanisms, and fail-safe mechanisms should be implemented to prevent accidents and handle unexpected situations.

Transferability to Real Environments: The system should be designed to generalize well to diverse real-world environments. Sim-to-real transfer techniques, domain adaptation, and real-world data collection can help in improving the system's performance in real settings.

Scalability and Complexity: Real-world tasks may involve complex interactions and long-horizon planning, posing scalability challenges. Hierarchical planning, task decomposition, and parallel processing can be used to handle complexity and scale up the system.

Ethical and Legal Considerations: Addressing ethical concerns related to autonomous systems, ensuring compliance with regulations, and considering societal implications are essential aspects that need to be taken into account when deploying robotic systems in real-world scenarios.

By proactively addressing these challenges and limitations, the PSL approach can be effectively adapted for real-world robotic applications, enhancing performance, reliability, and safety.

How could the integration of language models and reinforcement learning be leveraged to enable robots to learn from human instructions and demonstrations in a more natural and intuitive way?

The integration of language models and reinforcement learning can be leveraged to enable robots to learn from human instructions and demonstrations in a more natural and intuitive way through the following strategies:

Natural Language Understanding: Develop models that can interpret human instructions in natural language and convert them into actionable tasks for the robot. Language models can help in understanding the semantics and context of instructions.

Task Planning and Execution: Use language-guided planning to generate high-level task sequences based on human instructions. Reinforcement learning can then be employed to learn low-level control policies for executing these tasks effectively.

Interactive Learning: Enable robots to interact with humans to receive feedback, corrections, and additional instructions during the learning process. This interactive learning loop can enhance the robot's understanding and performance.

Imitation Learning: Incorporate imitation learning techniques where robots observe and mimic human demonstrations to learn new skills and behaviors. Language models can provide additional context and guidance for imitation learning.

Transfer Learning: Leverage transfer learning to apply knowledge gained from human instructions and demonstrations to new tasks or environments. This can help robots generalize their learning and adapt to different scenarios.

Human-Robot Collaboration: Foster collaboration between humans and robots in a shared workspace, where robots can assist humans based on verbal instructions and demonstrations. This collaborative approach can enhance productivity and efficiency.

Explainable AI: Develop models that can explain the reasoning behind the robot's actions and decisions, making the learning process more transparent and understandable to humans.

By integrating language models and reinforcement learning in these ways, robots can learn from human interactions in a more intuitive and human-like manner, enabling seamless communication, collaboration, and task execution in various real-world settings.