Agile Robotics: Optimal Control, Reinforcement Learning, and Differentiable Simulation
On the map: Deep reinforcement learning methods → RL for robot locomotion and manipulation
Abstract (arXiv)
Control systems are at the core of every real-world robot. They are deployed in an ever-increasing number of applications, ranging from autonomous racing and search-and-rescue missions to industrial inspections and space exploration. To achieve peak performance, certain tasks require pushing the robot to its maximum agility. How can we design control algorithms that enhance the agility of autonomous robots and maintain robustness against unforeseen disturbances? This paper addresses this question by leveraging fundamental principles in optimal control, reinforcement learning, and differentiable simulation.
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 22 of its 29 references that are in the map. First posted on arXiv 25 May 2024.
Builds on
22 of 29 references.
References · 22
- 2020Learning Quadrupedal Locomotion over Challenging Terraincited by 604
- 2019Learning agile and dynamic motor skills for legged robotscited by 504
- 2021Brax -- A Differentiable Physics Engine for Large Scale Rigid Body Simulationcited by 407
- 2019Learning by Cheatingcited by 383
- 2022Learning robust perceptive locomotion for quadrupedal robots in the wildcited by 382
- 2018Differentiable MPC for End-to-end Planning and Controlcited by 189
- 2023Extreme Parkour with Legged Robotscited by 164
- 2022Legged Locomotion in Challenging Terrains using Egocentric Visioncited by 151
- 2023Robot Parkour Learningcited by 149
- 2022Deep Whole-Body Control: Learning a Unified Policy for Manipulation and Locomotioncited by 121
- 2023Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learningcited by 111
- 2022Accelerated Policy Learning with Parallel Differentiable Simulationcited by 104
10 more
- 2021PlasticineLab: A Soft-Body Manipulation Benchmark with Differentiable Physicscited by 103
- 2022Do Differentiable Simulators Give Better Policy Gradients?cited by 89
- 2021Autonomous Drone Racing with Deep Reinforcement Learningcited by 60
- 2024Learning Quadruped Locomotion Using Differentiable Simulationcited by 32
- 2021Time-Optimal Planning for Quadrotor Waypoint Flightcited by 23
- 2021Model Predictive Contouring Control for Time-Optimal Quadrotor Flightcited by 21
- 2021Policy Search for Model Predictive Control with Application to Agile Drone Flightcited by 20
- 2023DiffMimic: Efficient Motion Mimicking with Differentiable Physicscited by 19
- 2020Learning High-Level Policies for Model Predictive Controlcited by 10
- 2022Weighted Maximum Likelihood for Controller Tuningcited by 3
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- 2026Foundations of Reinforcement Learning and Control:Connections and New Perspectives
- 2025Taxonomy and Trends in Reinforcement Learning for Robotics and Control Systems: A Structured Review
- 2024OGMP: Oracle Guided Multi-mode Policies for Agile and Versatile Robot Control
- 2025Space Robotics Bench: Robot Learning Beyond Earth
- 2025Mission-Aligned Learning-Informed Control of Autonomous Systems: Formulation and Foundations