HumanoidArena is a new benchmark of 7 leg-critical HOI/HSI tasks that evaluates egocentric hierarchical whole-body policies in humanoids and finds performance is strongly conditioned on the low-level GMT used.
Parkour in the wild: Learning a general and extensible agile locomotion policy using multi-expert distillation and rl fine-tuning
13 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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roto 2.0 provides a standardized benchmark for end-to-end blind tactile RL on 16-24 DOF robots, with open-sourced baselines achieving 13 Baoding ball rotations in 10 seconds.
Foot-mounted proximity sensors provide pre-contact feedback that, when integrated into RL, improves quadruped traversal robustness on discrete terrain with reliable sim-to-real transfer.
SWAP embeds symmetry equivariance into world models and policies, enabling a quadruped to leap 2.13m gaps and climb 1.63m platforms with robust generalization to mirrored and outdoor terrains.
TAGA learns terrain-aware active gaze behaviors for humanoid robots via RL alone, enabling generalizable locomotion with 1.2m real-world gap traversal.
A modular system uses motion matching to compose long-horizon human skill chains, trains RL experts, and distills them into a depth-based policy that lets a Unitree G1 humanoid autonomously climb, vault, and roll over obstacles up to 1.25 m tall.
Isaac Lab is a unified GPU-native platform combining high-fidelity physics, photorealistic rendering, multi-frequency sensors, domain randomization, and learning pipelines for scalable multi-modal robot policy training.
A hybrid motion-tracking and imitation-reinforcement pipeline produces a depth-based visuomotor policy that lets humanoids climb varied ladders zero-shot on hardware and perform teleoperated manipulation while climbing.
CoRe-MoE uses a two-stage RL framework with contrastive reweighting in a Mixture-of-Experts architecture to enable gait transitions and multi-terrain adaptation for humanoid locomotion.
SSR is an end-to-end vision-based framework for humanoid traversal that learns imagined foothold guidance, equivariant latent-space symmetry augmentation, and terrain-specific multi-discriminator motion priors to enable safe locomotion on diverse real-world terrains.
SDPG is a new on-policy visual RL algorithm that estimates gradients via stochastic perturbations of rollouts, achieving faster training and lower memory use than baselines on visual MuJoCo tasks while adding new robotics benchmarks and sim-to-real results.
Adding an actuated sagittal spine to a simulated quadruped increases agility and allows it to clear higher obstacles, steeper slopes, and tighter passages than the rigid-spine baseline.
Sparsely gated MoE policies double the success rate of a real Unitree Go2 quadruped on large-obstacle parkour versus matched-active-parameter MLP baselines while cutting inference time compared with a scaled-up MLP.
citing papers explorer
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HumanoidArena: Benchmarking Egocentric Hierarchical Whole-body Learning
HumanoidArena is a new benchmark of 7 leg-critical HOI/HSI tasks that evaluates egocentric hierarchical whole-body policies in humanoids and finds performance is strongly conditioned on the low-level GMT used.
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roto 2.0: The Robot Tactile Olympiad
roto 2.0 provides a standardized benchmark for end-to-end blind tactile RL on 16-24 DOF robots, with open-sourced baselines achieving 13 Baoding ball rotations in 10 seconds.
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Learning Locomotion on Discrete Terrain via Minimal Proximity Sensing
Foot-mounted proximity sensors provide pre-contact feedback that, when integrated into RL, improves quadruped traversal robustness on discrete terrain with reliable sim-to-real transfer.
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SWAP: Symmetric Equivariant World-Model for Agile Robot Parkour
SWAP embeds symmetry equivariance into world models and policies, enabling a quadruped to leap 2.13m gaps and climb 1.63m platforms with robust generalization to mirrored and outdoor terrains.
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TAGA: Terrain-aware Active Gaze Learning for Generalizable Agile Humanoid Locomotion
TAGA learns terrain-aware active gaze behaviors for humanoid robots via RL alone, enabling generalizable locomotion with 1.2m real-world gap traversal.
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Perceptive Humanoid Parkour: Chaining Dynamic Human Skills via Motion Matching
A modular system uses motion matching to compose long-horizon human skill chains, trains RL experts, and distills them into a depth-based policy that lets a Unitree G1 humanoid autonomously climb, vault, and roll over obstacles up to 1.25 m tall.
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Isaac Lab: A GPU-Accelerated Simulation Framework for Multi-Modal Robot Learning
Isaac Lab is a unified GPU-native platform combining high-fidelity physics, photorealistic rendering, multi-frequency sensors, domain randomization, and learning pipelines for scalable multi-modal robot policy training.
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LadderMan: Learning Humanoid Perceptive Ladder Climbing
A hybrid motion-tracking and imitation-reinforcement pipeline produces a depth-based visuomotor policy that lets humanoids climb varied ladders zero-shot on hardware and perform teleoperated manipulation while climbing.
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CoRe-MoE: Contrastive Reweighted Mixture of Experts for Multi-Terrain Humanoid Locomotion with Gait Adaptation
CoRe-MoE uses a two-stage RL framework with contrastive reweighting in a Mixture-of-Experts architecture to enable gait transitions and multi-terrain adaptation for humanoid locomotion.
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SSR: Scaling Surefooted and Symmetric Humanoid Traversal to the Open World
SSR is an end-to-end vision-based framework for humanoid traversal that learns imagined foothold guidance, equivariant latent-space symmetry augmentation, and terrain-specific multi-discriminator motion priors to enable safe locomotion on diverse real-world terrains.
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Efficient On-policy Visual-RL via Stochastic Decoupled Policy Gradient
SDPG is a new on-policy visual RL algorithm that estimates gradients via stochastic perturbations of rollouts, achieving faster training and lower memory use than baselines on visual MuJoCo tasks while adding new robotics benchmarks and sim-to-real results.
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Evaluation of an Actuated Spine in Agile Quadruped Locomotion
Adding an actuated sagittal spine to a simulated quadruped increases agility and allows it to clear higher obstacles, steeper slopes, and tighter passages than the rigid-spine baseline.
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Quadruped Parkour Learning: Sparsely Gated Mixture of Experts with Visual Input
Sparsely gated MoE policies double the success rate of a real Unitree Go2 quadruped on large-obstacle parkour versus matched-active-parameter MLP baselines while cutting inference time compared with a scaled-up MLP.