Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy Methods (1802.10264v2)

Abstract: In this paper, we explore deep reinforcement learning algorithms for vision-based robotic grasping. Model-free deep reinforcement learning (RL) has been successfully applied to a range of challenging environments, but the proliferation of algorithms makes it difficult to discern which particular approach would be best suited for a rich, diverse task like grasping. To answer this question, we propose a simulated benchmark for robotic grasping that emphasizes off-policy learning and generalization to unseen objects. Off-policy learning enables utilization of grasping data over a wide variety of objects, and diversity is important to enable the method to generalize to new objects that were not seen during training. We evaluate the benchmark tasks against a variety of Q-function estimation methods, a method previously proposed for robotic grasping with deep neural network models, and a novel approach based on a combination of Monte Carlo return estimation and an off-policy correction. Our results indicate that several simple methods provide a surprisingly strong competitor to popular algorithms such as double Q-learning, and our analysis of stability sheds light on the relative tradeoffs between the algorithms.

• The paper systematically compares off-policy deep RL algorithms, revealing that DQL excels in low-data scenarios through its bootstrapped Q-value approach.
• The paper finds that MC and Corrected MC methods enhance stability and performance in data-rich environments despite traditional bias challenges.
• The paper highlights that simpler, single-network strategies can improve stability in robotic grasping, encouraging research into hybrid learning techniques.

Evaluation of Off-Policy Deep Reinforcement Learning Techniques for Vision-Based Robotic Grasping

This paper presents a comprehensive empirical evaluation of various deep reinforcement learning (RL) algorithms applied to the problem of vision-based robotic grasping. With a focus on off-policy methods, the paper acknowledges the complexity associated with generalization to unseen objects, a critical requirement in realistic environments. The research fills a gap in the literature by systematically comparing several model-free RL methods and providing insights into their relative performance, data efficiency, stability, and sensitivity to hyperparameters.

The experimental setup involves a simulated benchmark where a 7 DoF robotic arm with a parallel jaw gripper attempts to grasp objects from a bin, utilizing RGB images as input. The benchmark includes two distinct tasks designed to test generalization: one involving grasping a diverse set of unseen objects and another requiring the targeting of specific objects amid visual and physical clutter.

Key methods evaluated include:

• Double Q-learning (DQL): Utilizes a target network and stochastic optimization for action selection. It demonstrates robustness across most conditions and is particularly effective in low-data scenarios, indicative of its bootstrapped approach's variance reduction benefits.
• Monte Carlo (MC) and Corrected Monte Carlo (Corr-MC): These techniques leverage entire episode returns for Q-value estimation. Although MC is biased in off-policy contexts, it performs competitively, suggesting potential applicability in rich data environments. The novel Corr-MC corrects this bias, exhibiting improved stability.
• Deep Deterministic Policy Gradient (DDPG): Typically combines actor and critic networks. However, the paper observes diminished stability and performance, likely due to the intricacies of actor-critic co-dependencies.
• Path Consistency Learning (PCL): Introduces entropy regularization within a stochastic optimal control framework. Though innovative, it lacks stability compared to simpler alternatives.

The paper thoroughly discusses algorithmic intricacies, drawing attention to the importance of the choice between using entire episode returns versus bootstrapping, and between stochastic action selection versus utilizing separate actor networks. Notably, the findings indicate that employing simpler, single-network approaches can enhance stability, an essential aspect for real-world robotics applications.

The results reveal that DQL, MC, and Corr-MC methods are among the most promising, with DQL excelling in low-data environments due to the bootstrapped Q-value calculations. Meanwhile, MC-based techniques may offer superior performance when larger datasets are available. These conclusions advocate for further exploration into hybrid methodologies that capitalize on the strengths of both bootstrapped and Monte Carlo approaches.

The implications of this research extend to practical deployments of robotic systems where data collection can be inherently limited, emphasizing the need for robust and stable learning frameworks. Future work is encouraged to translate these findings to real-world settings and investigate algorithm enhancements that dynamically adjust learning strategies based on present data regimes, potentially revolutionizing autonomous robotic operation and interaction in dynamic environments.