mlsanityData to 14 Oct 2026

VMTS: Vision-Assisted Teacher-Student Reinforcement Learning for Multi-Terrain Locomotion in Bipedal Robots

arXiv 2503.07049 ↗First posted 10 Mar 2025cs.ROcited by 1

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

Abstract (arXiv)

Bipedal robots, due to their anthropomorphic design, offer substantial potential across various applications, yet their control is hindered by the complexity of their structure. Currently, most research focuses on proprioception-based methods, which lack the capability to overcome complex terrain. While visual perception is vital for operation in human-centric environments, its integration complicates control further. Recent reinforcement learning (RL) approaches have shown promise in enhancing legged robot locomotion, particularly with proprioception-based methods. However, terrain adaptability, especially for bipedal robots, remains a significant challenge, with most research focusing on flat-terrain scenarios. In this paper, we introduce a novel mixture of experts teacher-student network RL strategy, which enhances the performance of teacher-student policies based on visual inputs through a simple yet effective approach. Our method combines terrain selection strategies with the teacher policy, resulting in superior performance compared to traditional models. Additionally, we introduce an alignment loss between the teacher and student networks, rather than enforcing strict similarity, to improve the student's ability to navigate diverse terrains. We validate our approach experimentally on the Limx Dynamic P1 bipedal robot, demonstrating its feasibility and robustness across multiple terrain types.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods. Builds on 25 of its 34 references that are in the map. First posted on arXiv 10 Mar 2025.

Builds on

25 of 34 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024DreamWaQ++: Obstacle-Aware Quadrupedal Locomotion With Resilient Multi-Modal Reinforcement Learning29 Sep
  2. 2024CTS: Concurrent Teacher-Student Reinforcement Learning for Legged Locomotion17 May

Other references · 23

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2021Barlow Twins: Self-Supervised Learning via Redundancy Reductioncited by 1,838
  3. 2021Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learningcited by 998
  4. 2020Learning Quadrupedal Locomotion over Challenging Terraincited by 604
  5. 2021Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learningcited by 512
  6. 2021RMA: Rapid Motor Adaptation for Legged Robotscited by 508
  7. 2022Learning robust perceptive locomotion for quadrupedal robots in the wildcited by 382
  8. 2023Extreme Parkour with Legged Robotscited by 164
  9. 2023ANYmal Parkour: Learning Agile Navigation for Quadrupedal Robotscited by 152
  10. 2023Robot Parkour Learningcited by 149
  11. 2022Walk These Ways: Tuning Robot Control for Generalization with Multiplicity of Behaviorcited by 138
  12. 2022Deep Whole-Body Control: Learning a Unified Policy for Manipulation and Locomotioncited by 121
11 more
  1. 2024Humanoid Parkour Learningcited by 96
  2. 2023DreamWaQ: Learning Robust Quadrupedal Locomotion With Implicit Terrain Imagination via Deep Reinforcement Learningcited by 66
  3. 2022Adapting Rapid Motor Adaptation for Bipedal Robotscited by 54
  4. 2024Humanoid-Gym: Reinforcement Learning for Humanoid Robot with Zero-Shot Sim2Real Transfercited by 53
  5. 2024PIE: Parkour with Implicit-Explicit Learning Framework for Legged Robotscited by 25
  6. 2024World Model-based Perception for Visual Legged Locomotioncited by 18
  7. 2023Contrastive Modules with Temporal Attention for Multi-Task Reinforcement Learningcited by 10
  8. 2024Walking with Terrain Reconstruction: Learning to Traverse Risky Sparse Footholdscited by 8
  9. 2024Toward Understanding Key Estimation in Learning Robust Humanoid Locomotioncited by 5
  10. 2022A Fast, Autonomous, Bipedal Walking Behavior over Rapid Regionscited by 3
  11. 2022Humanoid Path Planning over Rough Terrain using Traversability Assessmentcited by 1

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025No More Blind Spots: Learning Vision-Based Omnidirectional Bipedal Locomotion for Challenging Terraincited by 3

Similar, not cited

Close in wording, no citation either way

  1. 2025Teacher Motion Priors: Enhancing Robot Locomotion over Challenging Terrain
  2. 2024Learn to Teach: Sample-Efficient Privileged Learning for Humanoid Locomotion over Diverse Terrains
  3. 2026MARCH: Model-Assisted Reinforcement Learning for the Perceptive Control of Humanoids over Sparse Footholds
  4. 2025Learning Terrain Aware Bipedal Locomotion via Reduced Dimensional Perceptual Representations
  5. 2025Learning Terrain-Specialized Policies for Adaptive Locomotion in Challenging Environments
  6. 2024Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control
  7. 2023Infer and Adapt: Bipedal Locomotion Reward Learning from Demonstrations via Inverse Reinforcement Learning
  8. 2025Learning Perceptive Humanoid Locomotion over Challenging Terrain