Data-efficient Deep Reinforcement Learning for Dexterous Manipulation (1704.03073v1)

Abstract: Deep learning and reinforcement learning methods have recently been used to solve a variety of problems in continuous control domains. An obvious application of these techniques is dexterous manipulation tasks in robotics which are difficult to solve using traditional control theory or hand-engineered approaches. One example of such a task is to grasp an object and precisely stack it on another. Solving this difficult and practically relevant problem in the real world is an important long-term goal for the field of robotics. Here we take a step towards this goal by examining the problem in simulation and providing models and techniques aimed at solving it. We introduce two extensions to the Deep Deterministic Policy Gradient algorithm (DDPG), a model-free Q-learning based method, which make it significantly more data-efficient and scalable. Our results show that by making extensive use of off-policy data and replay, it is possible to find control policies that robustly grasp objects and stack them. Further, our results hint that it may soon be feasible to train successful stacking policies by collecting interactions on real robots.

• The paper introduces key enhancements to the DDPG algorithm, including Multiple Mini-batch Replay and Asynchronous updates, to significantly boost data efficiency.
• It demonstrates that complex dexterous tasks, such as stacking Lego bricks, can be mastered in under 10 million transitions using smart reward shaping and informed state initialization.
• The research highlights practical implications for real-world robotics by reducing training time and enhancing scalability through parallelized learning across simulated systems.

An Overview of Data-efficient Deep Reinforcement Learning for Dexterous Manipulation

The paper "Data-efficient Deep Reinforcement Learning for Dexterous Manipulation" addresses a challenging problem in robotics: enabling robots to perform dexterous manipulation tasks such as grasping and stacking objects. Traditional control methods in robotics often struggle with these complex tasks due to the intricate dynamics and variability present in real-world scenarios. This research uses advanced deep reinforcement learning (DRL) techniques to overcome these challenges, focusing on improving data efficiency and scalability in learning algorithms to make them feasible for real-world robotics applications.

Contributions and Approach

The primary contribution of this paper is a set of enhancements to the Deep Deterministic Policy Gradient (DDPG) algorithm, aimed at improving both the data efficiency and computational scalability. The research unfolds in a simulated environment where the task involves a robotic arm picking up a Lego brick and stacking it onto another. The difficulty of this task, which involves high-dimensional control spaces and multiple interactive sub-tasks, makes it representative of real-world manipulation challenges.

The paper introduces two key extensions to the DDPG algorithm:

1. Multiple Mini-batch Replay Steps (DPG-R): This modification allows for multiple updates of the neural network parameters per interaction with the environment, thereby decoupling the amount of data collection from the learning updates. This technique substantially increases data efficiency by allowing parameters to be better fit to the data before more experience is gathered, which is crucial given the expensive nature of data collection in robotic systems.
2. Asynchronous DDPG: Inspired by the asynchronous advantage actor-critic (A3C) algorithm, this approach involves a distributed version of DDPG. Here, learning and data collection are parallelized across multiple computers or robotic systems, which enhances computational efficiency and reduces wall-clock training time significantly.

Exploration Strategies

The paper also explores strategies to efficiently direct exploration and incorporate prior knowledge during learning:

• Composite Shaping Rewards: For tasks with multiple stages, designing reward functions that provide incremental feedback based on the progress in sub-tasks can significantly guide the learning process. Various shaping strategies were explored, showing that well-designed reward functions can lead to significantly improved learning performance.
• Initial State Distributions: Starting simulations in states that lie along successful trajectories or those close to solution states, rather than randomized locations, can help achieve better exploration and learning outcomes. This can provide a form of directed exploration that can be more effective than random exploration strategies, reducing the learning burden imposed on the agent.

Numerical Results and Conclusions

The numerical results presented in the paper highlight that with the proposed enhancements, robust control policies for the full stack task can be learned efficiently. Specifically, the strategies enabled successful policy learning for stacking tasks within less than 10 million environment transitions, corresponding to under 10 hours of interaction time on 16 simulated robots. Moreover, using the informed start states approach further reduced training to as low as 1 million transitions.

The implications of this research are significant for the practical application of DRL in robotics. The approach demonstrates that by improving the core learning algorithms and smartly managing the exploration-exploitation balance through reward shaping and state initialization, complex tasks can be learned more efficiently. These advancements suggest that it is increasingly feasible to transfer such DRL strategies to real robots, potentially allowing for direct policy learning from raw sensory inputs without prohibitive data collection requirements.

Future Directions

Future work may involve applying these techniques to more complex, real-world scenarios and integrating perception tasks directly from visual inputs. The simulation results present a promising lower bound for task complexity achievable by DRL algorithms, and future developments may focus on expanding the complexity and variability of real-world tasks that can be efficiently managed by autonomous learning systems in robotics. Continued advancements in this domain hold the potential for significant transformations in autonomous robot capabilities.