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Robot Parkour Learning
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Parkour is a grand challenge for legged locomotion that requires robots to overcome various obstacles rapidly in complex environments. Existing methods can generate either diverse but blind locomotion skills or vision-based but specialized skills by using reference animal data or complex rewards. However, autonomous parkour requires robots to learn generalizable skills that are both vision-based and diverse to perceive and react to various scenarios. In this work, we propose a system for learning a single end-to-end vision-based parkour policy of diverse parkour skills using a simple reward without any reference motion data. We develop a reinforcement learning method inspired by direct collocation to generate parkour skills, including climbing over high obstacles, leaping over large gaps, crawling beneath low barriers, squeezing through thin slits, and running. We distill these skills into a single vision-based parkour policy and transfer it to a quadrupedal robot using its egocentric depth camera. We demonstrate that our system can empower two different low-cost robots to autonomously select and execute appropriate parkour skills to traverse challenging real-world environments.
Forward citations
Cited by 15 Pith papers
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Light-Loco-Parkour: Versatile Perceptive Whole-Body Locomotion via Multi-Skill Distillation
A single neural-network policy, trained in simulation, makes a humanoid climb, vault, and traverse uneven terrain from onboard depth and a velocity command, with no skill labels or runtime motion graphs.
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Transformer Transformer: A Unified Model for Motion-Conditioned Robot Co-design
A single diffusion transformer trains on tokenized robot bodies and motions to generate and optimize robot designs for unseen rewards and trajectories, outpacing evolutionary search in speed and often in reward.
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StairMaster: Learning to Conquer Risky Hollow Stairs for Agile Quadrupedal Robots
StairMaster trains an RL policy that lets a Unitree Go2 quadruped climb hollow stairs up to 55 degrees via zero-shot sim-to-real transfer using cross-attention, SRU memory, and active-perception rewards.
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PTLD: Sim-to-real Privileged Tactile Latent Distillation for Dexterous Manipulation
PTLD distills real privileged tactile data into a state estimator to boost sim-to-real performance of proprioceptive dexterous manipulation policies, yielding 182% improvement on in-hand rotation and 57% on reorientat...
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Self-Supervised Multisensory Pretraining for Contact-Rich Robot Reinforcement Learning
MSDP pre-trains a transformer encoder with masked multisensory autoencoding, then uses an asymmetric actor-critic bridge (cross-attention for critic, pooling for actor) to accelerate and robustify contact-rich RL acro...
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Sampling Strategies for Robust Universal Quadrupedal Locomotion Policies
A particle-filter-based adaptive sampling of morphologies and wide PD-gain randomization yields a single quadruped locomotion policy that transfers zero-shot to ANYmal hardware.
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Benchmarking Massively Parallelized Multi-Task Reinforcement Learning for Robotics Tasks
The paper introduces MTBench, a GPU-accelerated benchmark for massively parallel multi-task RL, and reports experiments suggesting on-policy methods outperform off-policy baselines while value learning limits MTRL per...
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RAVEN: Reinforcement-Adaptive Visibility-Graph Planning for Robust Humanoid Navigation with Collision-Free MPC
Reinforcement learning that adjusts obstacle-inflation radii in a visibility-graph planner improves humanoid navigation robustness under delay and noise, beating a static MPC baseline and an end-to-end RL policy in a ...
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Coupled Local and Global World Models for Efficient First Order RL
Coupled local/global world models let first-order RL train image-space robot policies inside a learned diffusion simulator, outperforming PPO and a DreamerV3-only ablation on two tasks.
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DPL: Depth-only Perceptive Humanoid Locomotion via Realistic Depth Synthesis and Cross-Attention Terrain Reconstruction
Combining a blind-backbone policy, cross-attention terrain reconstruction from depth plus proprioception, and realistic synthetic depth with noise enables depth-only full-sized humanoid locomotion over stairs, slopes,...
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First Order Model-Based RL through Decoupled Backpropagation
By computing gradients through a learned dynamics model while unrolling trajectories in the real simulator, DMO achieves SHAC-level sample efficiency with standard simulators and deploys on a real quadruped.
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End-to-End Humanoid Robot Safe and Comfortable Locomotion Policy
An end-to-end humanoid locomotion policy maps raw LiDAR point clouds to motor commands using P3O with CBF-inspired safety costs and comfort rewards, with sim-to-real tests on a Unitree G1.
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RoboPearls: Editable Video Simulation for Robot Manipulation
RoboPearls is a 3D Gaussian Splatting based framework that edits demonstration videos into varied photorealistic simulations, and training on them improves robot manipulation success rates on RLBench and COLOSSEUM.
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WARL: Wrench-Augmented Reinforcement Learning for Task-Agnostic Learning in Legged Robots
Adding a simulated torso wrench during early RL training and gradually removing it lets a quadruped learn six locomotion tasks with a shared reward, yielding a joint-only policy in simulation.
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Ground-Aware Octree-A* Hybrid Path Planning for Memory-Efficient 3D Navigation of Ground Vehicles
An A*-on-octree planner with a height penalty plans ground-hugging 3D routes for ground vehicles in two simulations using under a tenth of the memory and computation time of uniform-grid A*, at nearly equal path length.
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