Tag Archives: Simulation-to-real Problem

Avoiding the sim-to-real RL transfer problem through learning the parameters of a physical system

September 6, 2024 07:49 ,

Viktor Wiberg, Erik Wallin, Arvid Fälldin, Tobias Semberg, Morgan Rossander, Eddie Wadbro, Martin Servin, Sim-to-real transfer of active suspension control using deep reinforcement learning, Robotics and Autonomous Systems, Volume 179, 2024 DOI: 10.1016/j.robot.2024.104731.

We explore sim-to-real transfer of deep reinforcement learning controllers for a heavy vehicle with active suspensions designed for traversing rough terrain. While related research primarily focuses on lightweight robots with electric motors and fast actuation, this study uses a forestry vehicle with a complex hydraulic driveline and slow actuation. We simulate the vehicle using multibody dynamics and apply system identification to find an appropriate set of simulation parameters. We then train policies in simulation using various techniques to mitigate the sim-to-real gap, including domain randomization, action delays, and a reward penalty to encourage smooth control. In reality, the policies trained with action delays and a penalty for erratic actions perform nearly at the same level as in simulation. In experiments on level ground, the motion trajectories closely overlap when turning to either side, as well as in a route tracking scenario. When faced with a ramp that requires active use of the suspensions, the simulated and real motions are in close alignment. This shows that the actuator model together with system identification yields a sufficiently accurate model of the actuators. We observe that policies trained without the additional action penalty exhibit fast switching or bang–bang control. These present smooth motions and high performance in simulation but transfer poorly to reality. We find that policies make marginal use of the local height map for perception, showing no indications of predictive planning. However, the strong transfer capabilities entail that further development concerning perception and performance can be largely confined to simulation.

Posted in: Applications of reinforcement learning to robots , Tagged: ,

Learning how to reset the episode in RL

April 3, 2024 12:59 ,

S. -H. Lee and S. -W. Seo, Self-Supervised Curriculum Generation for Autonomous Reinforcement Learning Without Task-Specific Knowledge, IEEE Robotics and Automation Letters, vol. 9, no. 5, pp. 4043-4050, May 2024 DOI: 10.1109/LRA.2024.3375714.

A significant bottleneck in applying current reinforcement learning algorithms to real-world scenarios is the need to reset the environment between every episode. This reset process demands substantial human intervention, making it difficult for the agent to learn continuously and autonomously. Several recent works have introduced autonomous reinforcement learning (ARL) algorithms that generate curricula for jointly training reset and forward policies. While their curricula can reduce the number of required manual resets by taking into account the agent’s learning progress, they rely on task-specific knowledge, such as predefined initial states or reset reward functions. In this paper, we propose a novel ARL algorithm that can generate a curriculum adaptive to the agent’s learning progress without task-specific knowledge. Our curriculum empowers the agent to autonomously reset to diverse and informative initial states. To achieve this, we introduce a success discriminator that estimates the success probability from each initial state when the agent follows the forward policy. The success discriminator is trained with relabeled transitions in a self-supervised manner. Our experimental results demonstrate that our ARL algorithm can generate an adaptive curriculum and enable the agent to efficiently bootstrap to solve sparse-reward maze navigation and manipulation tasks, outperforming baselines with significantly fewer manual resets.

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Including safety learning in RL for improving the sim-to-lab gap

November 17, 2023 09:59 ,

Kai-Chieh Hsu, Allen Z. Ren, Duy P. Nguyen, Anirudha Majumdar, Jaime F. Fisac, Sim-to-Lab-to-Real: Safe reinforcement learning with shielding and generalization guarantees, Artificial Intelligence, Volume 314, 2023 DOI: 10.1016/j.artint.2022.103811.

Safety is a critical component of autonomous systems and remains a challenge for learning-based policies to be utilized in the real world. In particular, policies learned using reinforcement learning often fail to generalize to novel environments due to unsafe behavior. In this paper, we propose Sim-to-Lab-to-Real to bridge the reality gap with a probabilistically guaranteed safety-aware policy distribution. To improve safety, we apply a dual policy setup where a performance policy is trained using the cumulative task reward and a backup (safety) policy is trained by solving the Safety Bellman Equation based on Hamilton-Jacobi (HJ) reachability analysis. In Sim-to-Lab transfer, we apply a supervisory control scheme to shield unsafe actions during exploration; in Lab-to-Real transfer, we leverage the Probably Approximately Correct (PAC)-Bayes framework to provide lower bounds on the expected performance and safety of policies in unseen environments. Additionally, inheriting from the HJ reachability analysis, the bound accounts for the expectation over the worst-case safety in each environment. We empirically study the proposed framework for ego-vision navigation in two types of indoor environments with varying degrees of photorealism. We also demonstrate strong generalization performance through hardware experiments in real indoor spaces with a quadrupedal robot. See https://sites.google.com/princeton.edu/sim-to-lab-to-real for supplementary material.

Posted in: Applications of reinforcement learning to robots , Tagged: ,

Improving the realism of a simulator through deep learning

June 28, 2023 10:21 ,

Allevato, A.D., Schaertl Short, E., Pryor, M. et al. , Iterative residual tuning for system identification and sim-to-real robot learning, . Auton Robot 44, 1167–1182 (2020) DOI: 10.1007/s10514-020-09925-w.

Robots are increasingly learning complex skills in simulation, increasing the need for realistic simulation environments. Existing techniques for approximating real-world physics with a simulation require extensive observation data and/or thousands of simulation samples. This paper presents iterative residual tuning (IRT), a deep learning system identification technique that modifies a simulator’s parameters to better match reality using minimal real-world observations. IRT learns to estimate the parameter difference between two parameterized models, allowing repeated iterations to converge on the true parameters similarly to gradient descent. In this paper, we develop and analyze IRT in depth, including its similarities and differences with gradient descent. Our IRT implementation, TuneNet, is pre-trained via supervised learning over an auto-generated simulated dataset. We show that TuneNet can perform rapid, efficient system identification even when the true parameter values lie well outside those in the network’s training data, and can also learn real-world parameter values from visual data. We apply TuneNet to a sim-to-real task transfer experiment, allowing a robot to perform a dynamic manipulation task with a new object after a single observation.

Posted in: Robot models , Tagged: ,

Improving the simulation-to-real transfer of learning robotic skills by learning smaller skills and how to connect them in reality

June 26, 2023 12:25 ,

Julian RC, Heiden E, He Z, et al., Scaling simulation-to-real transfer by learning a latent space of robot skills, . The International Journal of Robotics Research. 2020;39(10-11):1259-1278 DOI: 10.1177/0278364920944474.

We present a strategy for simulation-to-real transfer, which builds on recent advances in robot skill decomposition. Rather than focusing on minimizing the simulation–reality gap, we propose a method for increasing the sample efficiency and robustness of existing simulation-to-real approaches which exploits hierarchy and online adaptation. Instead of learning a unique policy for each desired robotic task, we learn a diverse set of skills and their variations, and embed those skill variations in a continuously parameterized space. We then interpolate, search, and plan in this space to find a transferable policy which solves more complex, high-level tasks by combining low-level skills and their variations. In this work, we first characterize the behavior of this learned skill space, by experimenting with several techniques for composing pre-learned latent skills. We then discuss an algorithm which allows our method to perform long-horizon tasks never seen in simulation, by intelligently sequencing short-horizon latent skills. Our algorithm adapts to unseen tasks online by repeatedly choosing new skills from the latent space, using live sensor data and simulation to predict which latent skill will perform best next in the real world. Importantly, our method learns to control a real robot in joint-space to achieve these high-level tasks with little or no on-robot time, despite the fact that the low-level policies may not be perfectly transferable from simulation to real, and that the low-level skills were not trained on any examples of high-level tasks. In addition to our results indicating a lower sample complexity for families of tasks, we believe that our method provides a promising template for combining learning-based methods with proven classical robotics algorithms such as model-predictive control.

Posted in: Applications of reinforcement learning to robots , Tagged: ,