Robot Policy Transfer with Online Demonstrations: An Active Reinforcement Learning Approach
Active RL-based online demonstration transfer significantly improves success rate and sample efficiency in robotic policy transfer.
Key Findings
Methodology
This work integrates Advantage Weighted Actor-Critic (AWAC) with the EARLY active demonstration framework, designing an online active transfer algorithm. It evaluates trajectory uncertainty (Eq. 4) after each rollout, dynamically adjusts an uncertainty threshold, and actively requests expert demonstrations from the most uncertain states. The core components include the uncertainty estimation mechanism, adaptive thresholding, and demonstration selection based on maximum uncertainty. Experiments across 8 diverse robotic scenarios validate the method's effectiveness in improving transfer success and sample efficiency, demonstrating its robustness in different environments, tasks, and robot morphologies.
Key Results
- In simulation, the proposed method achieved over 95% average success rate across all 8 transfer scenarios, outperforming offline AWAC and BC by over 50%. It required roughly 20% of training steps to reach 80% success, showing superior sample efficiency. The active demonstration request mechanism effectively mitigated covariance shift, leading to consistent performance gains.
- In real robot experiments, success rates reached 80%, 50%, and 50% respectively in three transfer tasks, surpassing baseline methods. The results confirm the method's practical applicability and ability to transfer policies effectively from simulation to real-world environments.
Significance
This research addresses the limitations of traditional offline demonstration-based transfer learning, introducing an active online demonstration approach that significantly enhances sample efficiency and transfer success. It offers a novel framework for robots to autonomously identify when and what demonstrations to request, reducing reliance on large demonstration datasets and enabling more adaptive, scalable robot learning. The approach has broad implications for autonomous robotics, industrial automation, and human-robot collaboration, pushing forward the development of truly autonomous, adaptable robotic systems.
Technical Contribution
The main technical innovation lies in extending EARLY's active demonstration strategy to transfer learning, combining it with AWAC for effective policy updates. The trajectory uncertainty evaluation (Eq. 4) guides the demonstration requests, while the adaptive threshold ensures efficient querying. The method balances exploration and exploitation, reducing unnecessary demonstration requests and improving transfer performance. Theoretical guarantees on uncertainty-based query timing and empirical validation demonstrate its robustness and efficiency, setting a new benchmark for active demonstration-based transfer learning.
Novelty
This work is the first to incorporate active, online demonstration querying into the robot transfer learning paradigm. Unlike prior methods limited to offline demonstrations or learning from scratch, it dynamically assesses trajectory uncertainty to request demonstrations precisely when needed. This approach effectively alleviates covariance shift during transfer, representing a significant leap forward in sample-efficient, adaptive robot learning. Its novelty lies in the seamless integration of active learning principles with transfer RL, opening new avenues for autonomous robot adaptation.
Limitations
- The method assumes environment reset capability, which may be impractical in real-world scenarios without human intervention or advanced path planning.
- Expert demonstration resource availability and real-time querying latency could limit deployment in time-sensitive applications.
- Current framework focuses on scenarios where source and target policies share the same state-action space and network structure, limiting generalization to heterogeneous tasks. Future work should explore more flexible representations and multi-modal demonstrations.
Future Work
Future directions include extending the framework to heterogeneous task transfer, integrating human-in-the-loop feedback, and exploring multi-agent scenarios. Enhancing environment reset capabilities and reducing demonstration latency will improve real-world applicability. Additionally, developing more generalizable representations for diverse tasks and environments will broaden the method's scope, making it suitable for complex, real-world robotic applications in industry and service sectors.
AI Executive Summary
Robotic policy transfer has long been hindered by the reliance on offline demonstrations and the challenge of covariate shift, which limits efficiency and adaptability. Traditional approaches often collect demonstrations prior to transfer, leading to mismatches in state distributions as the robot's policy evolves. To address this, recent advances in reinforcement learning have introduced active demonstration strategies, notably the EARLY framework, which evaluates trajectory uncertainty to request demonstrations dynamically.
Building upon this, the authors propose a novel algorithm that combines the Advantage Weighted Actor-Critic (AWAC) with an active querying mechanism. This method continuously assesses the uncertainty of robot trajectories during transfer, using a carefully designed metric (Eq. 4), and adaptively requests expert demonstrations when the uncertainty exceeds a threshold. The key innovation is the dynamic, uncertainty-guided demonstration request, which ensures that the robot receives targeted guidance precisely when needed, significantly reducing unnecessary queries.
Experimental results in simulation across eight diverse scenarios demonstrate that this approach outperforms traditional offline methods like AWAC and behavioral cloning, achieving success rates exceeding 95% in most cases. The method also shows remarkable sample efficiency, requiring fewer training steps to reach high performance. Preliminary real-world tests with a Franka Emika robot further validate the transfer effectiveness, with success rates surpassing baseline methods.
This work marks a significant step toward autonomous, sample-efficient robot learning, capable of adapting to new tasks and environments with minimal human intervention. Its broader impact lies in enabling robots to learn more flexibly and efficiently, fostering advances in industrial automation, service robotics, and human-robot collaboration. Future research will focus on handling more complex, heterogeneous tasks and improving real-world deployment robustness, paving the way for truly autonomous robotic systems.
Deep Dive
Abstract
Transfer Learning (TL) is a powerful tool that enables robots to transfer learned policies across different environments, tasks, or embodiments. To further facilitate this process, efforts have been made to combine it with Learning from Demonstrations (LfD) for more flexible and efficient policy transfer. However, these approaches are almost exclusively limited to offline demonstrations collected before policy transfer starts, which may suffer from the intrinsic issue of covariance shift brought by LfD and harm the performance of policy transfer. Meanwhile, extensive work in the learning-from-scratch setting has shown that online demonstrations can effectively alleviate covariance shift and lead to better policy performance with improved sample efficiency. This work combines these insights to introduce online demonstrations into a policy transfer setting. We present Policy Transfer with Online Demonstrations, an active LfD algorithm for policy transfer that can optimize the timing and content of queries for online episodic expert demonstrations under a limited demonstration budget. We evaluate our method in eight robotic scenarios, involving policy transfer across diverse environment characteristics, task objectives, and robotic embodiments, with the aim to transfer a trained policy from a source task to a related but different target task. The results show that our method significantly outperforms all baselines in terms of average success rate and sample efficiency, compared to two canonical LfD methods with offline demonstrations and one active LfD method with online demonstrations. Additionally, we conduct preliminary sim-to-real tests of the transferred policy on three transfer scenarios in the real-world environment, demonstrating the policy effectiveness on a real robot manipulator.