mlsanityData to 14 Oct 2026

Learning Reach-Avoid Task with Reinforcement Learning: Vectorized Simulation and Benchmark

arXiv 2607.15935 ↗First posted 17 Jul 2026cs.RO cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL for robot locomotion and manipulation

Abstract (arXiv)

Deep reinforcement learning (DRL) has a longstanding tradition in addressing the reach-avoid task problem, especially for controlling robotic arms. While this task serves as a baseline environment within the research community, the ability of DRL to effectively learn the each-avoid task in complex and realistic scenarios beyond simplified and restricted tabletop settings remains uncertain. In this paper, we present, for the first time, a comprehensive benchmark for the reachavoid task that accurately captures real-world complexities without simplifications. We demonstrate a diverse range of settings for robotic arm reach-avoid task, which can be used for evaluating DRL research. We achieved this by utilizing the MuJoCo MJX physics engine and parallelizing both the simulation environment and DRL algorithms using the Brax library. We achieved state-of-the-art results with success rates of 96.1% (UR5e) and 98.8% (Franka Emika Robot) for the reach task and 86.8% (UR5e) and 95.2% (Franka) for the static reachavoid task. Our results indicate that while in previous works DRL agents could solve, for example, a reach task in a simplified setting perfectly, their agents performance collapses when evaluated in realistic scenarios. Overall, this work identifies that additional research is still required to claim the successful resolution of the robotic arm reach-avoid task using DRL. The environment and benchmarking code is available as open source at the following link

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods. Builds on 17 of its 25 references that are in the map. First posted on arXiv 17 Jul 2026.

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17 of 25 references.

References · 17

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  3. 2016OpenAI Gymcited by 1,736
  4. 2018QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulationcited by 743
  5. 2021Brax -- A Differentiable Physics Engine for Large Scale Rigid Body Simulationcited by 407
  6. 2017OptLayer - Practical Constrained Optimization for Deep Reinforcement Learning in the Real Worldcited by 70
  7. 2021panda-gym: Open-source goal-conditioned environments for robotic learningcited by 59
  8. 2021Training Larger Networks for Deep Reinforcement Learningcited by 38
  9. 2018Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy Methodscited by 33
  10. 2019RL-RRT: Kinodynamic Motion Planning via Learning Reachability Estimators from RL Policiescited by 20
  11. 2024Solving Minimum-Cost Reach Avoid using Reinforcement Learningcited by 14
  12. 2020Accelerating Reinforcement Learning for Reaching using Continuous Curriculum Learningcited by 13
5 more
  1. 2024Revisiting Sparse Rewards for Goal-Reaching Reinforcement Learningcited by 13
  2. 2022The Impact of Task Underspecification in Evaluating Deep Reinforcement Learningcited by 10
  3. 2020Joint Space Control via Deep Reinforcement Learningcited by 7
  4. 2022IPPO: Obstacle Avoidance for Robotic Manipulators in Joint Space via Improved Proximal Policy Optimizationcited by 1
  5. 2023Deep-Reinforcement-Learning-based Path Planning for Industrial Robots using Distance Sensors as Observationcited by 1

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  3. 2024Integrating DeepRL with Robust Low-Level Control in Robotic Manipulators for Non-Repetitive Reaching Tasks
  4. 2025Taxonomy and Trends in Reinforcement Learning for Robotics and Control Systems: A Structured Review
  5. 2024SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning
  6. 2025Control-Optimized Deep Reinforcement Learning for Artificially Intelligent Autonomous Systems
  7. 2025SimLauncher: Launching Sample-Efficient Real-world Robotic Reinforcement Learning via Simulation Pre-training
  8. 2024Optimizing Deep Reinforcement Learning for Adaptive Robotic Arm Control