FLaRe: Achieving Masterful and Adaptive Robot Policies with Large-Scale Reinforcement Learning Fine-Tuning (2409.16578v2)

Abstract: In recent years, the Robotics field has initiated several efforts toward building generalist robot policies through large-scale multi-task Behavior Cloning. However, direct deployments of these policies have led to unsatisfactory performance, where the policy struggles with unseen states and tasks. How can we break through the performance plateau of these models and elevate their capabilities to new heights? In this paper, we propose FLaRe, a large-scale Reinforcement Learning fine-tuning framework that integrates robust pre-trained representations, large-scale training, and gradient stabilization techniques. Our method aligns pre-trained policies towards task completion, achieving state-of-the-art (SoTA) performance both on previously demonstrated and on entirely novel tasks and embodiments. Specifically, on a set of long-horizon mobile manipulation tasks, FLaRe achieves an average success rate of 79.5% in unseen environments, with absolute improvements of +23.6% in simulation and +30.7% on real robots over prior SoTA methods. By utilizing only sparse rewards, our approach can enable generalizing to new capabilities beyond the pretraining data with minimal human effort. Moreover, we demonstrate rapid adaptation to new embodiments and behaviors with less than a day of fine-tuning. Videos can be found on the project website at https://robot-flare.github.io/

• The paper introduces FLaRe, a framework that fine-tunes multi-task pre-trained robot policies using reinforcement learning to achieve state-of-the-art performance.
• It employs stabilization techniques—including reduced learning rates and separate actor-critic networks—to overcome limitations of traditional behavior cloning.
• The method adapts to novel tasks and real robots, delivering improvements of +23.6% in simulations and +30.7% on actual robotic platforms.

Overview of "FLaRe: Achieving Masterful and Adaptive Robot Policies with Large-Scale Reinforcement Learning Fine-Tuning"

The paper presents FLaRe, a large-scale Reinforcement Learning (RL) fine-tuning framework designed to significantly advance the performance of pre-trained robot policies. Traditional methods of pre-training robots through large-scale multi-task Behavior Cloning (BC) have faced limitations in handling unseen states and tasks. FLaRe aims to surpass these limitations by integrating robust pre-trained representations, large-scale training, and gradient stabilization techniques to achieve state-of-the-art (SoTA) performance in both familiar and novel tasks.

Key Contributions

The core contributions of this work include:

1. FLaRe Framework:
   • The introduction of a large-scale RL fine-tuning framework starting from a multi-task pre-trained policy.
   • Incorporation of techniques to stabilize RL fine-tuning, such as reducing learning rates, disabling entropy bonuses, and separating actor-critic networks.
2. Performance Metrics:
   • Demonstrated an average success rate of 79.5% in unseen environments for long-horizon mobile manipulation tasks.
   • Achieved significant improvements over prior SoTA methods with +23.6% in simulations and +30.7% on real robots.
3. Generalization Capability:
   • Successful adaptation to entirely novel tasks and embodiments with less than a day of fine-tuning.
   • Utilized sparse rewards to extend capabilities beyond pretraining data efficiently.

Methodology

FLaRe's methodology revolves around fine-tuning pre-trained policies using RL, which essentially aligns behavior with true task objectives, ensuring improved performance. The paper details multiple facets of the FLaRe framework that contribute to its efficacy:

• Starting from Pre-Trained Multi-Task Models: Leveraging the robust representations and behavior priors from large, pre-trained models like the SPOC transformer model.
• Large-Scale Simulation: Extensive simulation using AI2THOR to ensure diverse, large-scale training, facilitated by advanced simulation environments.
• Stabilizing RL Fine-Tuning: Applying key techniques such as:
  • On-policy RL algorithms like PPO for stable updates.
  • Lower learning rates compared to RL from scratch.
  • Excluding entropy bonuses that could destabilize the policy at the start of training.
  • Utilizing separate actor-critic networks to avoid the shared feature representation issues.

Evaluation and Results

The evaluations encompass a wide spectrum of tasks, both within the capabilities gleaned from pre-training data and novel, unseen tasks. The results underscore FLaRe's robust performance enhancements:

• In-Distribution Tasks: Achieved superior performance on the CHORES benchmark tasks, showcasing a marked improvement in success rates and efficiency.
• Out-of-Distribution Tasks: FLaRe excelled in tasks requiring object recognition, relational object attributes, and affordance understanding, which were not part of the original pre-training data.
• Real-World Adaptation: The framework's efficacy was further validated on real robots, with FLaRe transferring simulation-learned policies to real-world environments effectively.

Implications and Future Developments

FLaRe’s innovative approach holds significant implications for the robotics field. Practically, it offers a scalable and adaptable solution for fine-tuning robotic policies to tackle a broad array of tasks, reinforcing the deployment of versatile robots in real-world scenarios. Theoretically, it bridges gaps between BC and RL by effectively integrating robust, large-scale multi-task learning with the goal-oriented precision of RL.

Looking forward, FLaRe invites future research in several promising directions:

• End-to-End Fine-Tuning: Further exploration into end-to-end fine-tuning that incorporates more extensive task horizons and diverse action spaces.
• Real-World RL Fine-Tuning: Enhancing RL fine-tuning directly within real-world environments to reduce reliance on simulations and address tasks that resist easy simulation modeling, such as those involving dynamic interactions with complex physical properties.
• Continual Learning: Leveraging FLaRe's framework for continual learning scenarios where robots learn new tasks seamlessly over time without catastrophic forgetting.

In conclusion, FLaRe presents a robust and adaptable framework for evolving robot policies, reinforcing the significance of large-scale RL fine-tuning in advancing robotic capabilities and driving forward both theoretical and practical advancements in the field.