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

Deirdre Quillen, Eric Jang, Ofir Nachum, Chelsea Finn, Julian Ibarz, Sergey Levine

arXiv:1802.10264v2cs.ROcs.LGstat.ML

TL;DR

The paper addresses which off-policy deep RL methods best support vision-based grasping across diverse objects and generalization to unseen ones. It introduces a simulated benchmark and compares Q-function estimation methods, a prior grasping controller, and corrected Monte Carlo evaluation. Deep RL learned grasping from raw pixels with 90% average success on unseen objects, while simple Monte Carlo methods were surprisingly competitive and actor-based methods showed stability tradeoffs.

  • Problem

    The paper asks which off-policy deep RL algorithms are suited to vision-based grasping that requires diverse-object training and generalization to unseen objects.

  • Method

    The authors create a simulated vision-based grasping benchmark and compare off-policy model-free RL methods, a prior grasping controller, and a novel Monte Carlo method with off-policy correction.

  • Results

    Deep RL achieved 90% average success on previously unseen objects; naïve Monte Carlo was a strong baseline, and corrected Monte Carlo achieved comparable performance.

  • Takeaways & Limitations

    Simple return-estimation methods can compete strongly with popular deep RL algorithms, while actor networks trade faster action selection for reduced stability.

Abstract

from arXiv · show

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.

I. INTRODUCTION

The paper studies off-policy deep RL for vision-based grasping, where diverse training objects are needed to generalize to unseen objects. It introduces a simulated benchmark and compares established Q-function methods with a novel corrected Monte Carlo approach.

  • Motivation: Generalization to unseen objects is a central challenge because successful grasping requires training across diverse objects and scenes.Off-policy learning is emphasized because primarily on-policy training would require repeatedly revisiting objects to avoid forgetting.
  • Research question: The paper asks which off-policy RL algorithms are best suited to vision-based robotic grasping beyond conventional final-reward evaluations.Prior deep RL work had not explored the diverse situations arising in grasping or focused on generalization to new objects and situations.
  • Contributions: The authors introduce a simulated grasping benchmark and empirically evaluate off-policy deep RL methods on vision-based grasping tasks.The benchmark uses a robotic arm with a two-finger parallel-jaw gripper to grasp randomized objects from a bin.
  • Methods: The comparison includes grasp-success prediction, Q-learning, path consistency learning, DDPG, Monte Carlo policy evaluation, and Corrected Monte-Carlo.Corrected Monte-Carlo extends Monte Carlo policy evaluation with an off-policy correction for unbiased learning.
  • Results: 90% average success rate was achieved for grasping previously unseen objects in the simulator.Naïve Monte Carlo was a strong baseline despite off-policy bias, while the corrected version achieved comparable performance; deep Q-learning also excelled with limited data.

II. RELATED WORK

The related work spans value-based, policy-gradient, actor-critic, supervised, and model-based approaches to robotic learning. This paper distinguishes itself by systematically evaluating model-free off-policy RL for diverse, cluttered grasping and large off-policy datasets.

  • Deep reinforcement learning: Deep RL methods commonly use policy gradients, value functions, or actor-critic combinations, but they are known to be unstable and difficult to tune.Existing benchmarks have primarily emphasized video games and relatively simple control tasks.
  • Robot learning: Prior work has applied guided policy search and model-free RL algorithms to diverse manipulation skills on real robots.Examples include fitted Q-iteration, Monte Carlo returns, DDPG, trust-region policy optimization, and deep Q-networks.
  • Grasping generalization: This paper tests generalization to new objects in cluttered environments with visual occlusion and complex dynamics, unlike studies using simple geometric shapes.The goal is a systematic comparison of deep RL approaches to robotic grasping.
  • Supervision for grasping: Prior diverse-object grasping methods have used human labels, offline geometric success criteria, or robot self-supervision.Deep learning has also been incorporated into these grasping approaches.
  • Off-policy data: A primary consideration is learning effectively from large amounts of off-policy data to make deploying new algorithms more practical.Unlike supervised grasping approaches, the paper focuses on model-free RL, which can consider future action consequences.

IV. PROBLEM SETUP

The benchmark uses a simulated seven-degree-of-freedom arm with image-and-timestep observations, binary terminal rewards, and two grasping environments. The tasks evaluate either generalization to novel objects or targeted grasping in clutter.

  • Environment: The simulated arm has 7 degrees of freedom and 15 timesteps to find a grasp before the gripper closes and the episode ends.The reward is binary and provided only at the final step: 1 for success and 0 for failure.
  • Environment: The observation combines the robot camera’s current RGB image with the current timestep.The timestep indicates how many actions remain, including whether pre-grasp manipulation is still possible.
  • Regular grasping: The first task trains on 900 randomly generated rigid objects and tests on 100 new objects, with 5 objects placed in each bin.Objects are randomly switched every 20 episodes, and the test objects were not used during training.
  • Targeted grasping in clutter: The second task requires grasping a particular cross-shaped object in a cluttered bin where objects may visually occlude one another.Figure 1 contrasts this targeted task with regular grasping of varied randomized objects.
  • Evaluation objective: The paper evaluates off-policy deep RL algorithms for complex manipulation requiring generalization to novel objects.The comparison includes methods such as Q-learning and DDPG alongside other approaches.

A. Learning to Grasp with Supervised Learning

The comparison includes a prior supervised grasp-success controller and off-policy Q-learning, with implementation choices for multi-step labels, Bellman-error training, and continuous-action maximization.

  • Learning to Grasp with Supervised Learning: The prior grasping controller predicts single-step grasp success from raw monocular images and greedily selects the action with the highest predicted probability.It provides the closest comparison because it learns closed-loop vision-based control with deep networks for diverse-object grasping.
  • Learning to Grasp with Supervised Learning: Synthetic action labels move the gripper from its intermediate position p_t to the episode’s final pose p_T, using the episode outcome for each step.The label is a_t = p_T − p_t, but this procedure introduces bias because the full episode outcome is assigned to every step.
  • Off-Policy Q-Learning: Q-learning estimates the Q-function by minimizing Bellman error over state-action pairs sampled from an off-policy replay buffer.Training uses stochastic gradient descent, with gradients taken through the current Q-function term rather than the nondifferentiable max operator.
  • Off-Policy Q-Learning: A lagged target network and double Q-learning are used to improve Q-learning stability.The target network is delayed by 50 gradient updates and is decorrelated from the learned Q-function.
  • Off-Policy Q-Learning: For continuous actions, the target argmax is approximated by uniformly sampling 16 actions and selecting the one with the largest Q-value.The method is described as crude but efficient, easy to parallelize, and effective for the four-dimensional action parameterization.

C. Regression with Monte Carlo Return Estimates

Monte Carlo policy evaluation estimates Q-values from complete episode returns, avoiding bootstrapping but becoming biased with off-policy data. Corrected Monte Carlo adds an advantage-based correction to remove this bias while retaining a tunable stability mechanism.

  • C. Regression with Monte Carlo Return Estimates: Monte Carlo policy evaluation regresses Q-values directly onto complete episode returns rather than bootstrapped targets.With samples from the latest policy, this approach provides unbiased Q-value estimates and reduces optimization to supervised regression.
  • C. Regression with Monte Carlo Return Estimates: Monte Carlo evaluation is simple and stable because it does not require the current function approximator or target network to estimate targets.Its optimization reduces completely to standard regression.
  • D. Corrected Monte Carlo Evaluation: Monte Carlo estimates are biased off-policy because sampled trajectories may come from a policy different from the evaluated policy.The paper introduces corrected Monte Carlo to address this limitation.
  • D. Corrected Monte Carlo Evaluation: Corrected Monte Carlo adds a discounted sum of advantages, comparing sampled actions with optimal actions to remove off-policy bias.The correction gives higher values to good actions on bad trajectories and lower values to bad actions on good trajectories.
  • D. Corrected Monte Carlo Evaluation: The correction coefficient ν is annealed from 0 to 1, interpolating from supervised regression to an unbiased corrected method.This schedule is used to improve stability during training.

E. Deep Deterministic Policy Gradient

DDPG combines Q-learning and policy gradients by learning a deterministic actor whose gradients are propagated through a critic. Its Q-function update resembles Q-learning, while the actor replaces explicit action maximization and is updated separately.

  • E. Deep Deterministic Policy Gradient: DDPG learns a deterministic policy by propagating gradients through a critic Q-function.The method combines elements of Q-learning and policy gradients.
  • E. Deep Deterministic Policy Gradient: DDPG can be interpreted as approximate Q-learning because its actor approximately maximizes the Q-function over actions.The actor amortizes action search across states.
  • E. Deep Deterministic Policy Gradient: DDPG updates its Q-function with a bootstrapped objective and updates its actor with one gradient step toward maximization.A lagged target-network actor computes the target value in practice.

F. Path Consistency Learning

Path consistency learning is a stochastic optimal-control variant of Q-learning that resembles corrected Monte Carlo. It adds entropy regularization and enforces consistency between parameterized policy and value functions.

  • F. Path Consistency Learning: Path consistency learning is a stochastic optimal-control variant of Q-learning included because of its similarity to corrected Monte Carlo.The paper summarizes its implementation rather than deriving the full algorithm.
  • F. Path Consistency Learning: PCL augments the reinforcement-learning objective with a τ-weighted discounted entropy regularizer.The entropy term is part of the stochastic optimal-control formulation.
  • F. Path Consistency Learning: PCL requires the policy and value function to satisfy d-step consistency for every d > 0.This consistency condition links multi-step policy and value estimates.
  • F. Path Consistency Learning: PCL minimizes the squared difference between the two sides of its consistency equation for parameterized policy and value functions.The experiments use Trust-PCL, which employs a Gaussian policy and relative-entropy regularization against a prior policy.

G. Summary and Unified View

The evaluated algorithms differ primarily in how they estimate target values and select actions. The unified view contrasts bootstrapping with episode returns and actor-based maximization with stochastic search.

  • G. Summary and Unified View: All evaluated methods regress onto a target value to estimate a Q-function.Their principal differences are target-value construction and action-selection strategy.
  • G. Summary and Unified View: Q-learning and DDPG bootstrap from a current or target-network Q-function, whereas Monte Carlo variants, PCL, and supervised learning use complete episode returns.The bootstrap target uses the next-state action maximum.
  • G. Summary and Unified View: Correction terms in corrected Monte Carlo and PCL compensate for off-policy bias.The paper reports that adding the correction to Monte Carlo substantially improves performance.
  • G. Summary and Unified View: DDPG and PCL use an actor network for action selection, while the other methods maximize the Q-function through stochastic search.Actor-based selection is faster and can accelerate learning through amortization, but joint actor-value training can be unstable.
  • G. Summary and Unified View: The algorithm table organizes each method by target value and action-selection mechanism.The listed combinations include bootstrapped or episode-value targets and stochastic-search or actor-network selection.

VI. EXPERIMENTS

The experiments compare off-policy and on-policy learning across regular and targeted grasping tasks, emphasizing performance, data efficiency, off-policy robustness, and hyperparameter sensitivity. DQL performs strongly with limited data, while Monte Carlo methods become competitive with more data.

  • Experimental setup: The evaluation covers regular and targeted grasping, using held-out test objects and measuring performance, data efficiency, off-policy robustness, and hyperparameter sensitivity.All algorithms use variants of the Figure 3 Q-function architecture.
  • Data regimes: The experience pool begins with 10k, 100k, or 1M random-policy grasps, with on-policy training periodically adding 50 grasps every 1k training steps.The on-policy setting continually recollects data with the latest policy.
  • On-policy data: The amount of on-policy data used did not provide a significant performance benefit for any algorithm, suggesting that larger amounts may be impractical for robotics.This conclusion applies to the evaluated environments and data-collection setting.
  • Performance: DQL, supervised learning, MC, and Corr-MC learn the most successful policies given enough data across the evaluated tasks.DQL tends to perform better with limited data, whereas MC and Corr-MC are slightly better in the high-data regime on targeted grasping.
  • Performance: DQL performs better in low-data regimes, while MC and corrected MC achieve slightly better performance with abundant data on the harder targeted task.The bootstrapped target estimate may partly explain DQL’s low-data advantage through variance reduction.
  • Monte Carlo methods: Standard MC is often competitive with Corr-MC despite being highly biased off-policy, except in the lowest-data purely off-policy regimes.The results suggest that this bias may be less damaging than generally believed.

B. Analyzing Stability

The stability analysis evaluates performance across hyperparameter settings and random seeds. DQL, PCL, MC, Corr-MC, and supervised learning are relatively stable, whereas DDPG is substantially more sensitive and typically performs worst.

  • Regular grasping: DQL and the supervised baseline perform best on regular grasping across varying dataset sizes.The figure reports standard deviations from 9 independent runs with different random seeds.
  • Targeted grasping: DQL performs well in low-data and off-policy targeted grasping, whereas MC and Corr-MC perform best with maximal data.The targeted task uses three target objects and four non-target objects in a cluttered bin.
  • Sweep design: The stability sweep uses the on-policy setting with 100k initial random grasps, and MC and supervised learning do not use the discount-factor hyperparameter.These conditions define the scope of the reported sweep.
  • Sweep design: The sweep evaluates algorithm sensitivity across hyperparameter combinations and 9 random seeds.The settings include layer sizes, learning rate, discount factor, and exploration duration.
  • Hyperparameter stability: DQL, Corr-MC, PCL, MC, and supervised learning are relatively stable across hyperparameter values, while DDPG’s performance drops substantially for suboptimal settings.DDPG is comparatively unstable and typically achieves the worst performance.
  • Hyperparameter stability: Corr-MC is slightly more robust to hyperparameter choice than MC despite similar performance under optimal settings.The comparison is based on the hyperparameter-sensitivity analysis.

VII. DISCUSSION AND FUTURE WORK

The paper presents a diverse simulated benchmark and empirical comparison of off-policy model-free deep RL methods for robotic grasping. DQL is strongest in low-data regimes, while full-episode-return methods gain an advantage with plentiful data; actor networks reduce stability.

  • Contributions: The benchmark evaluates off-policy model-free deep RL methods on regular grasping with novel objects and targeted grasping in clutter.The grasping environment is a diverse simulator, and the benchmark is available as an open-source Gym environment.
  • Contributions: The evaluation includes double Q-learning, DDPG, PCL, supervised learning, naïve MC, and the novel corrected MC method.Corrected MC extends Monte Carlo evaluation for unbiased off-policy learning.
  • Main findings: DQL performs better on both tasks in low-data regimes for on-policy and off-policy learning and is relatively robust to hyperparameter choice.With more data, MC and corrected MC typically achieve slightly better performance.
  • Main findings: Actor networks substantially reduce stability, while single-network methods are associated with better stability and performance in the evaluated settings.Methods using entire episode values perform somewhat better with plentiful data, whereas bootstrapped DQL performs better with limited data.
  • Future work: Future work includes combining bootstrapping and multistep returns according to data availability and evaluating similar methods in real-world settings.The paper reports that all evaluated algorithms operate successfully in off-policy regimes.
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