Safety-Guided Imitation Learning for Robust and Reliable Robot Behaviors
核心概念
The core message of this work is to strategically expose the expert demonstrator to safety-critical scenarios during data collection, in order to enhance the safety and robustness of the learned imitation policy, especially in low-data regimes where the likelihood of error is higher.
摘要
The paper proposes a novel off-policy imitation learning method called SAFE-GIL (SAFEty Guided Imitation Learning) that addresses the compounding error problem in behavior cloning. The key idea is to abstract the potential policy errors as an adversarial disturbance in the system dynamics and intentionally guide the expert demonstrator towards safety-critical states during data collection. This is achieved by leveraging Hamilton-Jacobi reachability analysis to quantify the criticality of each state and compute the optimal disturbance that steers the system towards more unsafe regions.
The paper first provides background on Hamilton-Jacobi reachability analysis and how it can be used to measure state safety criticality and compute the optimal adversarial disturbance. It then describes the SAFE-GIL algorithm, where the disturbance is randomly scaled and injected into the expert's policy during data collection. This biases the training towards more closely replicating the expert's behavior in safety-critical states, while allowing more variance in less critical states.
The proposed method is evaluated on two case studies: autonomous navigation of a ground robot and autonomous taxiing of an aircraft. The results show that SAFE-GIL achieves substantially higher task success rates compared to alternative behavior cloning approaches, especially in low-data regimes where the likelihood of error is higher. This advantage is attributed to the diverse set of safety-critical states encountered during the guided data collection, which allows the learned policy to better recover from such situations during test time.
The paper also discusses the performance tradeoff of the proposed method, where the imitation policy may exhibit slightly degraded performance in reward-maximizing states due to the biased training towards safety-critical states. Finally, the authors highlight the importance of the disturbance bound as a critical hyperparameter and discuss future directions to estimate it in an informed manner.
SAFE-GIL
统计
The robot state is given by x := (px, py, θ), where (px, py) denote the position of the robot and θ represents its heading.
The robot control input is given by its angular velocity u := ω, which is bounded by the physical constraints of the vehicle's steering capability, i.e., ω ∈[−¯ω, ¯ω].
The failure set L is given by the obstacles that the robot must avoid on its way to the goal.
引用
"Behavior cloning has been used across a variety of robotic applications, ranging from manipulation, navigation, to autonomous driving. However, typically behavior cloning is an off-policy method that can suffer from compounding errors when the robot executes the learned policy, leading the system to drift to new and potentially dangerous states over time."
"Our key insight is to abstract the policy error as an adversarial disturbance in the system dynamics that attempts to steer the system into safety-critical states. By injecting such disturbances into expert demonstrations, we intentionally navigate the system towards riskier situations."
更深入的查询
How can the proposed method be extended to handle high-dimensional state and action spaces, such as in complex robotic manipulation tasks
To extend the proposed method to handle high-dimensional state and action spaces in complex robotic manipulation tasks, several strategies can be employed. One approach is to utilize dimensionality reduction techniques, such as autoencoders or principal component analysis, to transform the high-dimensional state space into a lower-dimensional representation while preserving essential information. This reduced representation can then be used for computing the adversarial disturbance and guiding the expert towards safety-critical states. Additionally, employing hierarchical reinforcement learning architectures can help manage the complexity of high-dimensional spaces by breaking down the task into sub-tasks with lower-dimensional state spaces. By incorporating hierarchical structures, the safety-guided data collection can focus on critical states at different levels of abstraction, enhancing the learning process in complex manipulation tasks.
What are the potential limitations of using Hamilton-Jacobi reachability analysis for computing the adversarial disturbance, and how can alternative approaches be explored
While Hamilton-Jacobi reachability analysis is a powerful tool for quantifying safety criticality and computing adversarial disturbances, it does have some limitations. One potential limitation is the computational complexity associated with solving the Hamilton-Jacobi equations, especially in high-dimensional state spaces. To address this, approximate solutions or sampling-based methods can be explored to make the computation more tractable. Another limitation is the assumption of perfect knowledge of the system dynamics, which may not always hold in real-world scenarios. Alternative approaches, such as model-free reinforcement learning methods or data-driven techniques, can be considered to learn the disturbance directly from data without relying on explicit dynamics models. By combining these approaches with Hamilton-Jacobi reachability analysis, a more robust and practical framework for computing adversarial disturbances can be developed.
Can the safety-guided data collection be combined with other imitation learning techniques, such as inverse reinforcement learning, to further enhance the performance and robustness of the learned policies
Combining safety-guided data collection with other imitation learning techniques, such as inverse reinforcement learning (IRL), can offer several benefits in enhancing the performance and robustness of learned policies. By incorporating IRL, the agent can learn the underlying reward structure of the expert's behavior, allowing for a more nuanced understanding of the task objectives. This can complement the safety-guided data collection by providing additional insights into expert behavior in different contexts. Furthermore, the combination of safety guidance with IRL can help in capturing complex decision-making processes and preferences of the expert, leading to more adaptive and versatile learned policies. Overall, integrating safety-guided data collection with IRL can result in more comprehensive and effective imitation learning frameworks for challenging robotic tasks.
