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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Learning to walk in minutes using massively parallel deep reinforcement learning
10 Pith papers cite this work. Polarity classification is still indexing.
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representative citing papers
CLF-guided RL yields exponentially stable optimal controllers, with proofs in continuous and discrete time, numerical checks on double integrator and cart-pole, and implementation on a walking humanoid.
CART learns a vision–proprioception terrain context and uses Temporal Sequence Selection to cut base oscillation by up to 41% in simulation and 22% on Spot outdoors, with a 5% higher sim success rate.
BIFROST learns invariant latent states via cross-domain bisimulation on paired data to enable zero-shot sim2real policy transfer for visual navigation, contact-rich manipulation, and visual servoing.
A thermal residual policy on a pre-trained quadruped locomotion controller prevents motor overheating under payload while preserving performance, lasting over 13 minutes on a Unitree A1 versus ~5 minutes for the nominal policy.
A single reinforcement learning policy jointly trains multiple locomotion skills for wheeled-legged robots with DC-motor constraints and learns a proprioceptive skill selector for adaptive behavior.
Empirical comparison shows a clear sim-to-real gap in reset-free RL for agile driving: TD-MPC2 outperforms the MPPI baseline in the real world while SAC excels in simulation, and residual learning benefits simulation but does not transfer.
A four-stage RL system with teacher-student distillation and online constrained adaptation enables humanoid robots to achieve robust ball-kicking accuracy under noisy perception in simulation and on physical hardware.
FastDSAC adds a truncated Gaussian policy constraint to distributional actor-critic methods to preserve network plasticity and accelerate training for scalable humanoid locomotion in parallel sampling setups.
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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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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Stability of Control Lyapunov Function Guided Reinforcement Learning
CLF-guided RL yields exponentially stable optimal controllers, with proofs in continuous and discrete time, numerical checks on double integrator and cart-pole, and implementation on a walking humanoid.
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CART: Context-Aware Terrain Adaptation using Temporal Sequence Selection for Legged Robots
CART learns a vision–proprioception terrain context and uses Temporal Sequence Selection to cut base oscillation by up to 41% in simulation and 22% on Spot outdoors, with a 5% higher sim success rate.
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BIFROST: Bridging Invariant Feature Representation for Observation-space Sim2Real Transfer
BIFROST learns invariant latent states via cross-domain bisimulation on paired data to enable zero-shot sim2real policy transfer for visual navigation, contact-rich manipulation, and visual servoing.
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Learning to Balance Motor Thermal Safety and Quadrupedal Locomotion Performance with Residual Policy
A thermal residual policy on a pre-trained quadruped locomotion controller prevents motor overheating under payload while preserving performance, lasting over 13 minutes on a Unitree A1 versus ~5 minutes for the nominal policy.
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MUJICA: Multi-skill Unified Joint Integration of Control Architecture for Wheeled-Legged Robots
A single reinforcement learning policy jointly trains multiple locomotion skills for wheeled-legged robots with DC-motor constraints and learns a proprioceptive skill selector for adaptive behavior.
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Reset-Free Reinforcement Learning for Real-World Agile Driving: An Empirical Study
Empirical comparison shows a clear sim-to-real gap in reset-free RL for agile driving: TD-MPC2 outperforms the MPPI baseline in the real world while SAC excels in simulation, and residual learning benefits simulation but does not transfer.
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Learning Agile Striker Skills for Humanoid Soccer Robots from Noisy Sensory Input
A four-stage RL system with teacher-student distillation and online constrained adaptation enables humanoid robots to achieve robust ball-kicking accuracy under noisy perception in simulation and on physical hardware.
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FastDSAC: Enhancing Policy Plasticity via Constrained Exploration for Scalable Humanoid Locomotion
FastDSAC adds a truncated Gaussian policy constraint to distributional actor-critic methods to preserve network plasticity and accelerate training for scalable humanoid locomotion in parallel sampling setups.
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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.