# DreamWaQ: Learning Robust Quadrupedal Locomotion With Implicit Terrain Imagination via Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2301.10602 · HTML: https://mlsanity.com/paper/2301.10602/

## Key facts

- Cited by 66 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2023.

## Built on by (most cited first, 30 of 66)

- [ASAP: Aligning Simulation and Real-World Physics for Learning Agile Humanoid Whole-Body Skills](https://mlsanity.com/paper/2502.01143/) (2025)
- [BeamDojo: Learning Agile Humanoid Locomotion on Sparse Footholds](https://mlsanity.com/paper/2502.10363/) (2025)
- [Hybrid Internal Model: Learning Agile Legged Locomotion with Simulated Robot Response](https://mlsanity.com/paper/2312.11460/) (2023)
- [CTS: Concurrent Teacher-Student Reinforcement Learning for Legged Locomotion](https://mlsanity.com/paper/2405.10830/) (2024)
- [LocoFormer: Generalist Locomotion via Long-context Adaptation](https://mlsanity.com/paper/2509.23745/) (2025)
- [Perceptive Humanoid Parkour: Chaining Dynamic Human Skills via Motion Matching](https://mlsanity.com/paper/2602.15827/) (2026)
- [World Model-based Perception for Visual Legged Locomotion](https://mlsanity.com/paper/2409.16784/) (2024)
- [GRaD-Nav++: Vision-Language Model Enabled Visual Drone Navigation with Gaussian Radiance Fields and Differentiable Dynamics](https://mlsanity.com/paper/2506.14009/) (2025)
- [Bridging the Sim-to-Real Gap for Athletic Loco-Manipulation](https://mlsanity.com/paper/2502.10894/) (2025)
- [Learning Humanoid Locomotion with World Model Reconstruction](https://mlsanity.com/paper/2502.16230/) (2025)
- [A Learning Framework for Diverse Legged Robot Locomotion Using Barrier-Based Style Rewards](https://mlsanity.com/paper/2409.15780/) (2024)
- [A Unified and General Humanoid Whole-Body Controller for Versatile Locomotion](https://mlsanity.com/paper/2502.03206/) (2025)
- [Bridging the Sim-to-Real Gap from the Information Bottleneck Perspective](https://mlsanity.com/paper/2305.18464/) (2023)
- [Learning to See Physical Properties with Active Sensing Motor Policies](https://mlsanity.com/paper/2311.01405/) (2023)
- [Robust Robot Walker: Learning Agile Locomotion over Tiny Traps](https://mlsanity.com/paper/2409.07409/) (2024)
- [PIP-Loco: A Proprioceptive Infinite Horizon Planning Framework for Quadrupedal Robot Locomotion](https://mlsanity.com/paper/2409.09441/) (2024)
- [Embrace Collisions: Humanoid Shadowing for Deployable Contact-Agnostics Motions](https://mlsanity.com/paper/2502.01465/) (2025)
- [GRaD-Nav: Efficiently Learning Visual Drone Navigation with Gaussian Radiance Fields and Differentiable Dynamics](https://mlsanity.com/paper/2503.03984/) (2025)
- [SLR: Learning Quadruped Locomotion without Privileged Information](https://mlsanity.com/paper/2406.04835/) (2024)
- [DreamWaQ++: Obstacle-Aware Quadrupedal Locomotion With Resilient Multi-Modal Reinforcement Learning](https://mlsanity.com/paper/2409.19709/) (2024)
- [RAPID: Robust and Agile Planner Using Inverse Reinforcement Learning for Vision-Based Drone Navigation](https://mlsanity.com/paper/2502.02054/) (2025)
- [Deep Whole-body Parkour](https://mlsanity.com/paper/2601.07701/) (2026)
- [TAR: Teacher-Aligned Representations via Contrastive Learning for Quadrupedal Locomotion](https://mlsanity.com/paper/2503.20839/) (2025)
- [FlashSAC: Fast and Stable Off-Policy Reinforcement Learning for High-Dimensional Robot Control](https://mlsanity.com/paper/2604.04539/) (2026)
- [Impact of Static Friction on Sim2Real in Robotic Reinforcement Learning](https://mlsanity.com/paper/2503.01255/) (2025)
- [MARG: MAstering Risky Gap Terrains for Legged Robots with Elevation Mapping](https://mlsanity.com/paper/2509.20036/) (2025)
- [A Real-World Quadrupedal Locomotion Benchmark for Offline Reinforcement Learning](https://mlsanity.com/paper/2309.16718/) (2023)
- [DexDribbler: Learning Dexterous Soccer Manipulation via Dynamic Supervision](https://mlsanity.com/paper/2403.14300/) (2024)
- [PrivilegedDreamer: Explicit Imagination of Privileged Information for Rapid Adaptation of Learned Policies](https://mlsanity.com/paper/2502.11377/) (2025)
- [Disturbance-Aware Adaptive Compensation in Hybrid Force-Position Locomotion Policy for Legged Robots](https://mlsanity.com/paper/2506.00472/) (2025)

## Abstract (arXiv)

Quadrupedal robots resemble the physical ability of legged animals to walk through unstructured terrains. However, designing a controller for quadrupedal robots poses a significant challenge due to their functional complexity and requires adaptation to various terrains. Recently, deep reinforcement learning, inspired by how legged animals learn to walk from their experiences, has been utilized to synthesize natural quadrupedal locomotion. However, state-of-the-art methods strongly depend on a complex and reliable sensing framework. Furthermore, prior works that rely only on proprioception have shown a limited demonstration for overcoming challenging terrains, especially for a long distance. This work proposes a novel quadrupedal locomotion learning framework that allows quadrupedal robots to walk through challenging terrains, even with limited sensing modalities. The proposed framework was validated in real-world outdoor environments with varying conditions within a single run for a long distance.