E2DRL: End-to-End Differentiable Reinforcement Learning for 3D Obstacle Avoidance in Quadruped Robots
Overview
E2DRL, a differentiable reinforcement learning framework for quadruped obstacle avoidance, achieves higher sample efficiency and sim-to-real validation on Unitree Go2 using SAPO and DAgger distillation with terrain-aware gating.
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Environment: simulation
“n a differentiable simulator is distilled into a recurrent depth-based student via DAgger, then blended with a base locomotion policy using a terrain-aware gating module for deployment. In simulation, E2DRL achieves higher sample efficiency than PPO and demonstrates superior velocity-tracking performance and 3D obstacle-avoidance capability compared with both PPO and Duality-based DCBF NMPC. We present the first sim-”
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- arXiv Robotics — research abstractsE2DRL: End-to-End Differentiable Reinforcement Learning for 3D Obstacle Avoidance in Quadruped Robots
This research proposes E2DRL, a differentiable reinforcement learning framework for 3D obstacle avoidance in quadruped robots. The method uses a soft obstacle-interaction formulation with velocity-conditioned stop-gradient shaping to provide first-order gradients for training. A privileged teacher trained via SAPO is distilled into a depth-based student using DAgger, and integrated with a terrain-aware gating module for deployment. The approach achieves higher sample efficiency and better performance in simulation compared to PPO and DCBF NMPC, with the first sim-to-real validation on a Unitree Go2 robot.
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