Monthly Archives: October 2024

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Improving exploration of the state space in RL for learning robotic skills through the use of RRTs

October 25, 2024 09:23 ,

Khandate, G., Saidi, T.L., Shang, S. et al. R R: Rapid eXploration for Reinforcement learning via sampling-based reset distributions and imitation pre-training, Auton Robot 48, 17 (2024) DOI: 10.1007/s10514-024-10170-8.

We present a method for enabling Reinforcement Learning of motor control policies for complex skills such as dexterous manipulation. We posit that a key difficulty for training such policies is the difficulty of exploring the problem state space, as the accessible and useful regions of this space form a complex structure along manifolds of the original high-dimensional state space. This work presents a method to enable and support exploration with Sampling-based Planning. We use a generally applicable non-holonomic Rapidly-exploring Random Trees algorithm and present multiple methods to use the resulting structure to bootstrap model-free Reinforcement Learning. Our method is effective at learning various challenging dexterous motor control skills of higher difficulty than previously shown. In particular, we achieve dexterous in-hand manipulation of complex objects while simultaneously securing the object without the use of passive support surfaces. These policies also transfer effectively to real robots. A number of example videos can also be found on the project website: sbrl.cs.columbia.edu

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

Robot exploration through decision-making + gaussian processes

October 24, 2024 06:18 ,

Stephens, A., Budd, M., Staniaszek, M. et al. Planning under uncertainty for safe robot exploration using Gaussian process prediction, Auton Robot 48, 18 (2024) DOI: 10.1007/s10514-024-10172-6.

The exploration of new environments is a crucial challenge for mobile robots. This task becomes even more complex with the added requirement of ensuring safety. Here, safety refers to the robot staying in regions where the values of certain environmental conditions (such as terrain steepness or radiation levels) are within a predefined threshold. We consider two types of safe exploration problems. First, the robot has a map of its workspace, but the values of the environmental features relevant to safety are unknown beforehand and must be explored. Second, both the map and the environmental features are unknown, and the robot must build a map whilst remaining safe. Our proposed framework uses a Gaussian process to predict the value of the environmental features in unvisited regions. We then build a Markov decision process that integrates the Gaussian process predictions with the transition probabilities of the environmental model. The Markov decision process is then incorporated into an exploration algorithm that decides which new region of the environment to explore based on information value, predicted safety, and distance from the current position of the robot. We empirically evaluate the effectiveness of our framework through simulations and its application on a physical robot in an underground environment.

Posted in: Robot motion planning , Tagged: , ,

How self-learning in mobile robot navigation can tackle situations rarely coped with by other methods in spite of their long training time

October 10, 2024 09:30 ,

Al Mahmud, S., Kamarulariffin, A., Ibrahim, A.M. et al. , Advancements and Challenges in Mobile Robot Navigation: A Comprehensive Review of Algorithms and Potential for Self-Learning Approaches, J Intell Robot Syst 110, 120 (2024) DOI: 10.1007/s10846-024-02149-5.

Mobile robot navigation has been a very popular topic of practice among researchers since a while. With the goal of enhancing the autonomy in mobile robot navigation, numerous algorithms (traditional AI-based, swarm intelligence-based, self-learning-based) have been built and implemented independently, and also in blended manners. Nevertheless, the problem of efficient autonomous robot navigation persists in multiple degrees due to the limitation of these algorithms. The lack of knowledge on the implemented techniques and their shortcomings act as a hindrance to further development on this topic. This is why an extensive study on the previously implemented algorithms, their applicability, their weaknesses as well as their potential needs to be conducted in order to assess how to improve mobile robot navigation performance. In this review paper, a comprehensive review of mobile robot navigation algorithms has been conducted. The findings suggest that, even though the self-learning algorithms require huge amounts of training data and have the possibility of learning erroneous behavior, they possess huge potential to overcome challenges rarely addressed by the other traditional algorithms. The findings also insinuate that in the domain of machine learning-based algorithms, integration of knowledge representation with a neuro-symbolic approach has the capacity to improve the accuracy and performance of self-robot navigation training by a significant margin.

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

Improving sample efficiency under sparse rewards and large continuous action spaces through predictive control in RL

October 10, 2024 09:26 ,

Antonyshyn, L., Givigi, S., Deep Model-Based Reinforcement Learning for Predictive Control of Robotic Systems with Dense and Sparse Rewards, J Intell Robot Syst 110, 100 (2024) DOI: 10.1007/s10846-024-02118-y.

Sparse rewards and sample efficiency are open areas of research in the field of reinforcement learning. These problems are especially important when considering applications of reinforcement learning to robotics and other cyber-physical systems. This is so because in these domains many tasks are goal-based and naturally expressed with binary successes and failures, action spaces are large and continuous, and real interactions with the environment are limited. In this work, we propose Deep Value-and-Predictive-Model Control (DVPMC), a model-based predictive reinforcement learning algorithm for continuous control that uses system identification, value function approximation and sampling-based optimization to select actions. The algorithm is evaluated on a dense reward and a sparse reward task. We show that it can match the performance of a predictive control approach to the dense reward problem, and outperforms model-free and model-based learning algorithms on the sparse reward task on the metrics of sample efficiency and performance. We verify the performance of an agent trained in simulation using DVPMC on a real robot playing the reach-avoid game. Video of the experiment can be found here: https://youtu.be/0Q274kcfn4c.

Posted in: Reinforcement learning in AI , Tagged: , , ,

Reducing the need of samples in RL through evolutionary techniques

October 10, 2024 09:22 ,

Onori, G., Shahid, A.A., Braghin, F. et al. , Adaptive Optimization of Hyper-Parameters for Robotic Manipulation through Evolutionary Reinforcement Learning, J Intell Robot Syst 110, 108 (2024) DOI: 10.1007/s10846-024-02138-8.

Deep Reinforcement Learning applications are growing due to their capability of teaching the agent any task autonomously and generalizing the learning. However, this comes at the cost of a large number of samples and interactions with the environment. Moreover, the robustness of learned policies is usually achieved by a tedious tuning of hyper-parameters and reward functions. In order to address this issue, this paper proposes an evolutionary RL algorithm for the adaptive optimization of hyper-parameters. The policy is trained using an on-policy algorithm, Proximal Policy Optimization (PPO), coupled with an evolutionary algorithm. The achieved results demonstrate an improvement in the sample efficiency of the RL training on a robotic grasping task. In particular, the learning is improved with respect to the baseline case of a non-evolutionary agent. The evolutionary agent needs % fewer samples to completely learn the grasping task, enabled by the adaptive transfer of knowledge between the agents through the evolutionary algorithm. The proposed approach also demonstrates the possibility of updating reward parameters during training, potentially providing a general approach to creating reward functions.

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