Safety-Driven Deep Reinforcement Learning Framework for Cobots: A Sim2Real Approach

• URL: http://arxiv.org/abs/2407.02231v1
• Date: Tue, 2 Jul 2024 12:56:17 GMT
• Title: Safety-Driven Deep Reinforcement Learning Framework for Cobots: A Sim2Real Approach
• Authors: Ammar N. Abbas, Shakra Mehak, Georgios C. Chasparis, John D. Kelleher, Michael Guilfoyle, Maria Chiara Leva, Aswin K Ramasubramanian,
• Abstract summary: This study presents a novel methodology incorporating safety constraints into a robotic simulation during the training of deep reinforcement learning (DRL) The framework integrates specific parts of the safety requirements, such as velocity constraints, directly within the DRL model. The proposed approach outperforms the conventional method by a 16.5% average success rate on the tested scenarios.
• Score: 1.0488553716155147
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study presents a novel methodology incorporating safety constraints into a robotic simulation during the training of deep reinforcement learning (DRL). The framework integrates specific parts of the safety requirements, such as velocity constraints, as specified by ISO 10218, directly within the DRL model that becomes a part of the robot's learning algorithm. The study then evaluated the efficiency of these safety constraints by subjecting the DRL model to various scenarios, including grasping tasks with and without obstacle avoidance. The validation process involved comprehensive simulation-based testing of the DRL model's responses to potential hazards and its compliance. Also, the performance of the system is carried out by the functional safety standards IEC 61508 to determine the safety integrity level. The study indicated a significant improvement in the safety performance of the robotic system. The proposed DRL model anticipates and mitigates hazards while maintaining operational efficiency. This study was validated in a testbed with a collaborative robotic arm with safety sensors and assessed with metrics such as the average number of safety violations, obstacle avoidance, and the number of successful grasps. The proposed approach outperforms the conventional method by a 16.5% average success rate on the tested scenarios in the simulations and 2.5% in the testbed without safety violations. The project repository is available at https://github.com/ammar-n-abbas/sim2real-ur-gym-gazebo.

Related papers

• ActSafe: Active Exploration with Safety Constraints for Reinforcement Learning [48.536695794883826]
  We present ActSafe, a novel model-based RL algorithm for safe and efficient exploration. We show that ActSafe guarantees safety during learning while also obtaining a near-optimal policy in finite time. In addition, we propose a practical variant of ActSafe that builds on latest model-based RL advancements.
  arXiv  Detail & Related papers  (2024-10-12T10:46:02Z)
• Safety through Permissibility: Shield Construction for Fast and Safe Reinforcement Learning [57.84059344739159]
  "Shielding" is a popular technique to enforce safety inReinforcement Learning (RL) We propose a new permissibility-based framework to deal with safety and shield construction.
  arXiv  Detail & Related papers  (2024-05-29T18:00:21Z)
• Reinforcement Learning for Safe Robot Control using Control Lyapunov Barrier Functions [9.690491406456307]
  Reinforcement learning (RL) exhibits impressive performance when managing complicated control tasks for robots. This paper explores the control Lyapunov barrier function (CLBF) to analyze the safety and reachability solely based on data. We also proposed the Lyapunov barrier actor-critic (LBAC) to search for a controller that satisfies the data-based approximation of the safety and reachability conditions.
  arXiv  Detail & Related papers  (2023-05-16T20:27:02Z)
• A Multiplicative Value Function for Safe and Efficient Reinforcement Learning [131.96501469927733]
  We propose a safe model-free RL algorithm with a novel multiplicative value function consisting of a safety critic and a reward critic. The safety critic predicts the probability of constraint violation and discounts the reward critic that only estimates constraint-free returns. We evaluate our method in four safety-focused environments, including classical RL benchmarks augmented with safety constraints and robot navigation tasks with images and raw Lidar scans as observations.
  arXiv  Detail & Related papers  (2023-03-07T18:29:15Z)
• Safety Correction from Baseline: Towards the Risk-aware Policy in Robotics via Dual-agent Reinforcement Learning [64.11013095004786]
  We propose a dual-agent safe reinforcement learning strategy consisting of a baseline and a safe agent. Such a decoupled framework enables high flexibility, data efficiency and risk-awareness for RL-based control. The proposed method outperforms the state-of-the-art safe RL algorithms on difficult robot locomotion and manipulation tasks.
  arXiv  Detail & Related papers  (2022-12-14T03:11:25Z)
• Evaluating Model-free Reinforcement Learning toward Safety-critical Tasks [70.76757529955577]
  This paper revisits prior work in this scope from the perspective of state-wise safe RL. We propose Unrolling Safety Layer (USL), a joint method that combines safety optimization and safety projection. To facilitate further research in this area, we reproduce related algorithms in a unified pipeline and incorporate them into SafeRL-Kit.
  arXiv  Detail & Related papers  (2022-12-12T06:30:17Z)
• Safe and Efficient Reinforcement Learning Using Disturbance-Observer-Based Control Barrier Functions [5.571154223075409]
  This paper presents a method for safe and efficient reinforcement learning (RL) using disturbance observers (DOBs) and control barrier functions (CBFs) Our method does not involve model learning, and leverages DOBs to accurately estimate the pointwise value of the uncertainty, which is then incorporated into a robust CBF condition to generate safe actions. Simulation results on a unicycle and a 2D quadrotor demonstrate that the proposed method outperforms a state-of-the-art safe RL algorithm using CBFs and Gaussian processes-based model learning.
  arXiv  Detail & Related papers  (2022-11-30T18:49:53Z)
• Safe Model-Based Reinforcement Learning with an Uncertainty-Aware Reachability Certificate [6.581362609037603]
  We build a safe reinforcement learning framework to resolve constraints required by the DRC and its corresponding shield policy. We also devise a line search method to maintain safety and reach higher returns simultaneously while leveraging the shield policy.
  arXiv  Detail & Related papers  (2022-10-14T06:16:53Z)
• Safe Reinforcement Learning with Contrastive Risk Prediction [35.80144544954927]
  We propose a risk preventive training method for safe RL, which learns a statistical contrastive classifier to predict the probability of a state-action pair leading to unsafe states. Based on the predicted risk probabilities, we can collect risk preventive trajectories and reshape the reward function with risk penalties to induce safe RL policies. The results show the proposed approach has comparable performance with the state-of-the-art model-based methods and outperforms conventional model-free safe RL approaches.
  arXiv  Detail & Related papers  (2022-09-10T18:54:38Z)
• Evaluating the Safety of Deep Reinforcement Learning Models using Semi-Formal Verification [81.32981236437395]
  We present a semi-formal verification approach for decision-making tasks based on interval analysis. Our method obtains comparable results over standard benchmarks with respect to formal verifiers. Our approach allows to efficiently evaluate safety properties for decision-making models in practical applications.
  arXiv  Detail & Related papers  (2020-10-19T11:18:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.