HiPAN enables quadruped robots to navigate unstructured 3D environments more successfully by combining a high-level posture-adaptive policy with a low-level controller and curriculum learning on depth images.
Agile but safe: Learn- ing collision-free high-speed legged locomotion
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
fields
cs.RO 5years
2026 5verdicts
UNVERDICTED 5representative citing papers
AWARE is a hierarchical RL framework that enables wheeled-legged robots to perform high-dynamic reflexive obstacle evasion with emergent gaits in simulation and on the real M20 platform.
KinematicRL is a sim-to-real RL framework for social navigation using second-order control inputs, iLQR pretraining, and cluster-based 2D LiDAR tracking to produce kinodynamically feasible policies deployable on real robots with minimal modifications.
A lightweight RL framework trains terrain-agnostic 3D foothold-tracking policies for humanoids that transfer directly to real-world use as standalone low-level controllers.
NavRL++ improves sim-to-real transfer for RL navigation via empirical analysis of perturbations, perturbation-aware fine-tuning, and a Transformer temporal policy, with real-world validation showing outperformance over learning baselines and parity with optimization planners in static cases.
citing papers explorer
-
HiPAN: Hierarchical Posture-Adaptive Navigation for Quadruped Robots in Unstructured 3D Environments
HiPAN enables quadruped robots to navigate unstructured 3D environments more successfully by combining a high-level posture-adaptive policy with a low-level controller and curriculum learning on depth images.
-
Unleashing the Agility of Wheeled-Legged Robots for High-Dynamic Reflexive Obstacle Evasion
AWARE is a hierarchical RL framework that enables wheeled-legged robots to perform high-dynamic reflexive obstacle evasion with emergent gaits in simulation and on the real M20 platform.
-
KinematicRL: A Sim-to-Real Reinforcement Learning Framework For Social Navigation With Kinodynamic Feasibility
KinematicRL is a sim-to-real RL framework for social navigation using second-order control inputs, iLQR pretraining, and cluster-based 2D LiDAR tracking to produce kinodynamically feasible policies deployable on real robots with minimal modifications.
-
Mind Your Steps: A General Learning Framework for Accurate Humanoid Foothold Tracking
A lightweight RL framework trains terrain-agnostic 3D foothold-tracking policies for humanoids that transfer directly to real-world use as standalone low-level controllers.
-
NavRL++: A System-Level Framework for Improving Sim-to-Real Transfer in Reinforcement Learning-Based Robot Navigation
NavRL++ improves sim-to-real transfer for RL navigation via empirical analysis of perturbations, perturbation-aware fine-tuning, and a Transformer temporal policy, with real-world validation showing outperformance over learning baselines and parity with optimization planners in static cases.