End-to-End Robotic Reinforcement Learning without Reward Engineering (1904.07854v2)

Abstract: The combination of deep neural network models and reinforcement learning algorithms can make it possible to learn policies for robotic behaviors that directly read in raw sensory inputs, such as camera images, effectively subsuming both estimation and control into one model. However, real-world applications of reinforcement learning must specify the goal of the task by means of a manually programmed reward function, which in practice requires either designing the very same perception pipeline that end-to-end reinforcement learning promises to avoid, or else instrumenting the environment with additional sensors to determine if the task has been performed successfully. In this paper, we propose an approach for removing the need for manual engineering of reward specifications by enabling a robot to learn from a modest number of examples of successful outcomes, followed by actively solicited queries, where the robot shows the user a state and asks for a label to determine whether that state represents successful completion of the task. While requesting labels for every single state would amount to asking the user to manually provide the reward signal, our method requires labels for only a tiny fraction of the states seen during training, making it an efficient and practical approach for learning skills without manually engineered rewards. We evaluate our method on real-world robotic manipulation tasks where the observations consist of images viewed by the robot's camera. In our experiments, our method effectively learns to arrange objects, place books, and drape cloth, directly from images and without any manually specified reward functions, and with only 1-4 hours of interaction with the real world.

• The paper introduces a method that bypasses manual reward engineering by enabling robots to learn from limited human feedback and active state queries.
• It employs deep neural networks and the soft actor-critic algorithm to efficiently process camera inputs for diverse manipulation tasks.
• The approach achieves effective skill acquisition in just 1 to 4 hours of real-world interaction, underscoring its practical scalability.

End-to-End Robotic Reinforcement Learning without Reward Engineering

The paper by Avi Singh et al. investigates a novel approach to robotic reinforcement learning that circumvents the traditional requirement for reward engineering. By leveraging deep neural networks and reinforcement learning, the authors propose a method where a robot can learn from limited examples and actively solicited queries, effectively bypassing the need for manually programmed reward functions. This methodology is particularly advantageous in real-world applications, where manual reward specification can be cumbersome and impractical.

The primary innovation in this paper lies in its strategy to eliminate the necessity for extensive reward engineering by using a framework that learns from examples indicating successful outcomes. Unlike conventional reinforcement learning methods that require significant prior knowledge to specify reward functions, this approach allows for dynamic learning through limited human feedback. Specifically, the method requires a robot to query a user for labels indicative of task success. This process involves providing labels for only a minority of the states encountered during training, enhancing efficiency in learning skills without manually constructed rewards.

The results presented confirm the efficacy of this approach across various robotic manipulation tasks. By training with camera image inputs, the system effectively manages tasks such as arranging objects, placing books, and draping cloth, all while avoiding explicitly coded reward functions. Notably, this is achieved with minimal real-world interaction time—between 1 to 4 hours—highlighting the practical application potential. Such numerical results underline the method's capability to learn complex tasks from minimal data and interaction periods, a considerable improvement over previous works that required more rigorous environmental instrumentation or reward function tuning.

The paper contributes a model where positive results can be incorporated into the learning process via a classifier trained to distinguish between successful and unsuccessful outcomes. The method's efficiency is bolstered by off-policy reinforcement learning, specifically utilizing the soft actor-critic algorithm—optimizing computational resources and real-world learning feasibly. Moreover, the combination of a modest number of positive outcome examples and active querying enhances the system's ability to generalize from fewer data points, making it ideal for environments where exhaustive sensor instrumentation is not viable.

The implications of this work are significant, offering a promising direction for the development of autonomous systems capable of learning directly from interactions. Theoretically, this expands the potential reach of reinforcement learning in robotic applications by streamlining the interaction between human feedback and machine learning. Practically, it emphasizes tasks that facilitate deployment in less controlled environments—where traditional reward engineering is less feasible.

Looking forward, this research opens pathways for further exploration into reducing the number of required queries and enhancing the universality of the learned reward functions across different tasks. Future efforts could delve into integrating methods to quantify model uncertainty more effectively, potentially leading to even greater efficiencies in learning. Additionally, extending these methodologies with meta-learning techniques could provide more significant advancements in leveraging shared task structures, further reducing the need for extensive human input.

In summary, the work of Singh et al. represents a crucial advancement toward autonomous robotic systems that learn efficiently from minimal data and user interaction, broadening the scope and applicability of reinforcement learning in open-world environments.