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RMBench: Benchmarking Deep Reinforcement Learning for Robotic Manipulator Control
Yanfei Xiang, Xin Wang, Shu Hu, Bin Zhu, Xiaomeng Huang, Xi Wu, Siwei Lyu
TL;DR
Evaluating reinforcement learning on robotic manipulation remains challenging because existing benchmarks do not cover visual inputs and continuous control across manipulation tasks. RMBench evaluates model-free algorithms directly from raw pixels on a suite of robotic tasks. No tested algorithm handles every task well; SAC performs effectively in average reward and training stability, while data augmentation and autoencoders can help agents learn richer environmental information.
Problem
Existing benchmarks do not provide a consistent testbed for RL algorithms that must control robotic manipulation from high-dimensional images with continuous actions.
Source: §I · p. 1Method
Results
Takeaways & Limitations
DrQ-v2's data augmentation and convolutional encoder can facilitate understanding pixel-level states, and combining data augmentation with advanced RL algorithms may yield more satisfactory results.
Source: C. Results and Discussion · p. 7
Abstract
from arXiv · showhide
Reinforcement learning is applied to solve actual complex tasks from high-dimensional, sensory inputs. The last decade has developed a long list of reinforcement learning algorithms. Recent progress benefits from deep learning for raw sensory signal representation. One question naturally arises: how well do they perform concerning different robotic manipulation tasks? Benchmarks use objective performance metrics to offer a scientific way to compare algorithms. In this paper, we present RMBench, the first benchmark for robotic manipulations, which have high-dimensional continuous action and state spaces. We implement and evaluate reinforcement learning algorithms that directly use observed pixels as inputs. We report their average performance and learning curves to show their performance and stability of training. Our study concludes that none of the studied algorithms can handle all tasks well, soft Actor-Critic outperforms most algorithms in average reward and stability, and an algorithm combined with data augmentation may facilitate learning policies. Our code is publicly available at https://github.com/xiangyanfei212/RMBench-2022, including all benchmark tasks and studied algorithms.
I. INTRODUCTION
The introduction motivates a benchmark for evaluating deep RL on visual robotic manipulation, where continuous control and 3D observations make existing comparisons insufficient. RMBench evaluates algorithms across manipulation tasks and reports performance and learning stability.
I. INTRODUCTION
- RMBench addresses the need for a rigorous testbed for RL algorithms that control robotic manipulation from high-dimensional visual inputs in continuous state and action spaces.The benchmark covers lifting, placing, reaching, stacking, and reassembling.Source: §I · pp. 1–2
- The benchmark evaluates policies trained directly from raw pixels in a 3D environment, using seven model-free RL algorithms across nine tasks.The introduction names VPG, TRPO, PPO, DDPG, TD3, SAC, and DrQ-v2.Source: §I · pp. 1–2
- None of the tested algorithms handles every task well; SAC is effective in average reward and training stability, while VPG performs satisfactorily on most tasks.DrQ-v2's data augmentation and autoencoder can help agents gain richer environmental information, even when learning is unstable.Source: §I · p. 2
- The benchmark supplies standardized experiments as baselines for visual manipulation, with the stated aim of facilitating investigation and improvement of robot manipulation solutions.The introduction notes that agents trained in 3D simulation can be transferred to real-world tasks.Source: §I · p. 2
II. RELATED WORK
Related benchmarks cover Atari, continuous control, and robotic manipulation, but the cited work does not provide a multi-task benchmark for visual robotic manipulation with continuous control.
II. RELATED WORK
- Existing benchmarks such as robosuite, ROBEL, and RLLib commonly use low-dimensional physical states, whereas RMBench evaluates policies from raw pixels in high-dimensional continuous spaces.The paper also distinguishes Atari benchmarks with discrete actions from its robotic-control setting.Source: §II · p. 2
- Prior robotic benchmarks include real-world two-finger-gripper tasks and simulation-to-real reaching, but the latter's single task type is insufficient for thorough algorithm evaluation.The two-finger benchmark compares open-loop behavior cloning with MOReL, rather than current state-of-the-art RL algorithms.Source: §II · p. 2
- RMBench differs by implementing five manipulation task types: lifting, placing, reaching, stacking, and reassembling.The cited passage presents this breadth in contrast to the preceding robotic benchmarks.Source: §II · p. 2
III. OUR ROBOTIC MANIPULATION BENCHMARK
RMBench evaluates seven model-free reinforcement-learning algorithms on nine robotic manipulation tasks with high-dimensional continuous states and actions, using raw pixel observations. The benchmark spans five manipulation types and compares policy-optimization and actor-critic methods, including DrQ-v2.
III. OUR ROBOTIC MANIPULATION BENCHMARK
- RMBench implements nine tasks across lifting, placing, reaching, stacking, and reassembling, with continuous high-dimensional state and action spaces and raw-pixel inputs.The benchmark uses seven algorithms selected to represent differences in publication date, policy type, learning strategy, optimization method, and continuous-action applicability.Source: §III · pp. 2–3
- The benchmark covers seven model-free algorithms developed between 2000 and 2021, comparing policy-learning type, policy type, and optimization approach.The table abbreviates these categories as PLT, PT, and Opt, and also identifies deterministic or stochastic policies, policy optimization, Q-learning, and data augmentation.Source: Table I · p. 3
- VPG, TRPO, and PPO are on-policy policy-optimization methods, while DDPG, TD3, and SAC use actor-critic approaches with continuous actions.VPG updates from recent-policy experience; TRPO constrains policy changes by KL divergence, and PPO uses a clipped surrogate objective.Source: §III · pp. 3–4
- SAC combines expected-return and entropy objectives, while DrQ-v2 applies random-shift image augmentation to pixel observations using a DDPG backbone.The benchmark fixes SAC's entropy-regularization coefficient; DrQ-v2 also uses n-step returns.Source: §III · p. 4
A. Experimental Settings
The experiments use simulated 3D robotic manipulation tasks with 84 × 84 pixel observations and per-step rewards from 0 to 1. Agents train for 200 episodes across five random seeds, with results summarized by task and algorithm.
A. Experimental Settings
- The benchmark uses simulated 3D robotic-arm tasks with 84 × 84 pixel observations, per-step rewards in [0, 1], and a 10-second time limit.The setup uses dm_control with MuJoCo; the passage describes policies as trained directly from observed pixel states.Source: A. Experimental Settings · p. 4
- Agents train for 200 episodes with five random seeds, and each episode contains 2000 steps.The study uses existing public implementations with moderate modifications and the original papers' default hyperparameters.Source: A. Experimental Settings · p. 4
- Actor-critic training begins with 10,000 random-action steps, delays updates until 1,000 interactions, then updates networks every 50 interactions with Gaussian exploration noise of standard deviation 0.1.The passage also specifies GAE with λ = 0.95 for on-policy algorithms.Source: A. Experimental Settings · p. 4
- Training curves show episode reward by episode across nine tasks, while Table II reports MER means and standard deviations across 200 episodes and five random seeds.DrQ-v2 uses random-shift augmentation of pixel observations; its architecture uses convolutional layers followed by fully connected layers.Source: Figure 2 · p. 5, Table II · p. 5, A. Experimental Settings · p. 5
B. Performance Metrics
The paper measures episode performance with Episode Reward and task-level average performance with Mean Episode Reward. Figure 3 presents average performance by algorithm and encodes algorithm families and policy types visually.
B. Performance Metrics
- Episode Reward (ER) measures an episode's performance using the undiscounted return over its T steps.The return at each step is denoted R_i^t.Source: B. Performance Metrics · pp. 5–6
- Mean Episode Reward (MER) measures average performance for a specific task by averaging episode rewards across episodes.The number of episodes is denoted by I.Source: B. Performance Metrics · p. 6
- Figure 3 plots MER by algorithm, using green for policy optimization, blue for actor-critic, and hatching to distinguish stochastic from deterministic policies.The caption describes average performance across all benchmark tasks.Source: Figure 3 · p. 6
C. Results and Discussion
Across RMBench tasks, SAC performs best in average reward and stability, but no algorithm is consistently strong. Results vary by task and algorithm family; DrQ-v2 often improves on DDPG but trains unstably.
Algorithm Level
- VPG is acceptable on most tasks but can converge rapidly to local optima; TRPO is competitive on placing and reaching, while PPO performs worse than TRPO.The authors cite PPO instability when rewards vanish outside bounded support and sensitivity to initialization near locally optimal actions.Source: C. Results and Discussion · p. 6
- SAC shows the best average reward and stability among the tested algorithms, with entropy regularization associated with a better exploration–exploitation trade-off.The authors suggest this trade-off helps exploration and keeps policies from prematurely converging to an inferior local optimum.Source: C. Results and Discussion · p. 6
- DDPG performs moderately, and TD3 performs similarly to DDPG, so TD3’s clipped double Q-learning and delayed policy updates do not significantly boost performance here.The authors note DDPG depends on suitable temporally correlated exploration noise.Source: C. Results and Discussion · p. 6
Category Level
- On-policy and off-policy methods perform similarly overall, with TRPO strongest on-policy and SAC strongest off-policy; SAC is marginally better than TRPO.The authors suggest off-policy sample collection may bring better exploration.Source: C. Results and Discussion · p. 6
- Actor-critic methods perform comparably to policy optimization methods, while stochastic-policy algorithms also achieve comparable performance to deterministic-policy algorithms.The authors interpret the stochastic-policy result as highlighting the importance of exploration during learning.Source: C. Results and Discussion · pp. 6–7
Data Augmentation
- DrQ-v2 outperforms DDPG on most tasks and leads on reassembling five bricks, but its training is unstable; the authors recommend a substantial replay buffer for data augmentation.They attribute the gains to data augmentation and a convolutional encoder, while suggesting convolution may lose state detail and contribute to instability.Source: C. Results and Discussion · pp. 6–7
Task Level
- SAC and TRPO lead on placing, SAC leads on reaching, DrQ-v2 leads on reassembling five bricks, and all algorithms struggle with lifting and stacking.The authors suggest lifting and stacking demand more precise, complex arm and finger coordination.Source: C. Results and Discussion · p. 7
V. CONCLUSION AND FUTURE WORK
RMBench evaluates seven model-free RL algorithms on nine pixel-based robotic manipulation tasks, and none handles every task well. The authors identify lifting performance, task diversity, and sim-to-real transfer as areas for further work.
Conclusion
- RMBench covers nine robotic manipulation tasks and evaluates seven model-free RL algorithms from raw pixels; none handles all tasks well.The benchmark provides standardized experiments as baselines for visual robotic manipulation tasks.Source: §V · p. 7
- All algorithms perform poorly on lifting, motivating the development of better algorithms; the authors state that agents trained in 3D simulation can be transferred to real-world tasks.RMBench’s stated goal is to facilitate study and improvement of robotic manipulation solutions through a simulation framework and environment benchmark.Source: §V · p. 7
Future Work
- Future work should broaden task diversity, evaluate sim-to-real transfer given simulator–world dynamics discrepancies, improve state extraction, and compare training time under fixed hardware configurations.The authors also propose deeper networks and computer-vision data augmentation as avenues to improve state understanding and sample efficiency.Source: §V · p. 7
Related papers
This paper cites
- Asynchronous Methods for Deep Reinforcement Learning2016
- Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor2018
- High-Dimensional Continuous Control Using Generalized Advantage Estimation2015
- Addressing Function Approximation Error in Actor-Critic Methods2018
- DeepMind Control Suite2018
- Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning2021