Integrating Trajectory Optimization and Reinforcement Learning for Quadrupedal Jumping with Terrain-Adaptive Landing
On the map: Deep reinforcement learning methods → RL for robot locomotion and manipulation
Abstract (arXiv)
Jumping constitutes an essential component of quadruped robots' locomotion capabilities, which includes dynamic take-off and adaptive landing. Existing quadrupedal jumping studies mainly focused on the stance and flight phase by assuming a flat landing ground, which is impractical in many real world cases. This work proposes a safe landing framework that achieves adaptive landing on rough terrains by combining Trajectory Optimization (TO) and Reinforcement Learning (RL) together. The RL agent learns to track the reference motion generated by TO in the environments with rough terrains. To enable the learning of compliant landing skills on challenging terrains, a reward relaxation strategy is synthesized to encourage exploration during landing recovery period. Extensive experiments validate the accurate tracking and safe landing skills benefiting from our proposed method in various scenarios.
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 19 of its 30 references that are in the map. First posted on arXiv 16 Sep 2025.
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19 of 30 references.
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Other references · 18
- 2017Proximal Policy Optimization Algorithmscited by 15,215
- 2021Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learningcited by 998
- 2020Learning Quadrupedal Locomotion over Challenging Terraincited by 604
- 2021Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learningcited by 512
- 2019Learning agile and dynamic motor skills for legged robotscited by 504
- 2022Learning robust perceptive locomotion for quadrupedal robots in the wildcited by 382
- 2024Expressive Whole-Body Control for Humanoid Robotscited by 139
- 2022Walk These Ways: Tuning Robot Control for Generalization with Multiplicity of Behaviorcited by 138
- 2023DreamWaQ: Learning Robust Quadrupedal Locomotion With Implicit Terrain Imagination via Deep Reinforcement Learningcited by 66
- 2023DTC: Deep Tracking Controlcited by 56
- 2022Perceptive Locomotion through Nonlinear Model Predictive Controlcited by 38
- 2023RL + Model-based Control: Using On-demand Optimal Control to Learn Versatile Legged Locomotioncited by 26
6 more
- 2021Cat-like Jumping and Landing of Legged Robots in Low-gravity Using Deep Reinforcement Learningcited by 17
- 2021Contact-timing and Trajectory Optimization for 3D Jumping on Quadruped Robotscited by 10
- 2022Continuous Jumping for Legged Robots on Stepping Stones via Trajectory Optimization and Model Predictive Controlcited by 10
- 2021Rapid and Reliable Quadruped Motion Planning with Omnidirectional Jumpingcited by 5
- 2024Quadruped-Frog: Rapid Online Optimization of Continuous Quadruped Jumpingcited by 4
- 2023Reactive Landing Controller for Quadruped Robotscited by 3
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Citing papers per year: 2025: 1.
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- 2025Guided Reinforcement Learning for Omnidirectional 3D Jumping in Quadruped Robots
- 2025Explosive Jumping with Rigid and Articulated Soft Quadrupeds via Example Guided Reinforcement Learning
- 2024Reinforcement Learning For Quadrupedal Locomotion: Current Advancements And Future Perspectives
- 2025Towards Quadrupedal Jumping and Walking for Dynamic Locomotion using Reinforcement Learning
- 2025MULE: Multi-terrain and Unknown Load Adaptation for Effective Quadrupedal Locomotion
- 2026Towards Low-Gravity Planetary Exploration using Reinforcement Learning for Walking, Jumping, and In-flight Attitude Control
- 2025Risk-Aware Reinforcement Learning with Bandit-Based Adaptation for Quadrupedal Locomotion
- 2026Towards Torque-Driven Reinforcement Learning for Quadruped Locomotion