SigLoMa enables dynamic loco-manipulation on quadrupeds from ego-centric 5 Hz vision alone by using Sigma Points for scalable exteroception, an ego-centric Kalman Filter for high-rate state estimation, and an active sampling curriculum, matching expert human teleoperation performance.
Learning humanoid locomotion with perceptive internal model
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HAIC enables robust humanoid interactions with underactuated objects by predicting their dynamics from proprioceptive history and using a world model for adaptive control.
TeleGate achieves high-precision real-time whole-body teleoperation of humanoid robots by dynamically gating between expert policies and using a VAE motion prior to infer future intent from history, outperforming distillation baselines on dynamic motions with only 2.5 hours of mocap data.
DreamPolicy integrates an autoregressive diffusion world model with policy learning to produce a single scalable policy that generalizes to unseen composite terrains for humanoid locomotion.
VAIC distills a teacher policy into a vision-and-proprioception student policy using recurrent adaptation and decoupled commands, enabling diverse real-robot tasks like box carrying and skateboarding that outperform baselines.
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.
An end-to-end policy learns robust humanoid locomotion directly from noisy depth images via high-fidelity sensor simulation, vision-aware distillation from privileged maps, and terrain-specific multi-critic reward shaping.
A two-stage distillation plus reinforced fine-tuning approach produces a single humanoid locomotion controller that adapts across skills and irregular terrains.
A one-shot adaptation technique for humanoid whole-body motion that computes order-preserving optimal transport distances between walking and target sequences, interpolates geodesic intermediate poses, optimizes for collision-free retargeting, and adapts via reinforcement learning.
A multi-channel terrain affordance reward combined with lower-body compliance training via virtual wrenches enables end-to-end PPO-trained humanoid policies to walk at 1 m/s on 0.2 m risers with improved payload robustness.
citing papers explorer
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SigLoMa: Learning Open-World Quadrupedal Loco-Manipulation from Ego-Centric Vision
SigLoMa enables dynamic loco-manipulation on quadrupeds from ego-centric 5 Hz vision alone by using Sigma Points for scalable exteroception, an ego-centric Kalman Filter for high-rate state estimation, and an active sampling curriculum, matching expert human teleoperation performance.
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HAIC: Humanoid Agile Object Interaction Control via Dynamics-Aware World Model
HAIC enables robust humanoid interactions with underactuated objects by predicting their dynamics from proprioceptive history and using a world model for adaptive control.
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TeleGate: Whole-Body Humanoid Teleoperation via Gated Expert Selection with Motion Prior
TeleGate achieves high-precision real-time whole-body teleoperation of humanoid robots by dynamically gating between expert policies and using a VAE motion prior to infer future intent from history, outperforming distillation baselines on dynamic motions with only 2.5 hours of mocap data.
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DreamPolicy: A Unified World-model Policy for Scalable Humanoid Locomotion
DreamPolicy integrates an autoregressive diffusion world model with policy learning to produce a single scalable policy that generalizes to unseen composite terrains for humanoid locomotion.
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VAIC: Vision-Guided Humanoid Agile Object Interaction Control via Decoupled Commands
VAIC distills a teacher policy into a vision-and-proprioception student policy using recurrent adaptation and decoupled commands, enabling diverse real-robot tasks like box carrying and skateboarding that outperform baselines.
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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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Now You See That: Learning End-to-End Humanoid Locomotion from Raw Pixels
An end-to-end policy learns robust humanoid locomotion directly from noisy depth images via high-fidelity sensor simulation, vision-aware distillation from privileged maps, and terrain-specific multi-critic reward shaping.
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Towards Adaptive Humanoid Control via Multi-Behavior Distillation and Reinforced Fine-Tuning
A two-stage distillation plus reinforced fine-tuning approach produces a single humanoid locomotion controller that adapts across skills and irregular terrains.
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One-shot Adaptation of Humanoid Whole-body Motion with Walking Priors
A one-shot adaptation technique for humanoid whole-body motion that computes order-preserving optimal transport distances between walking and target sequences, interpolates geodesic intermediate poses, optimizes for collision-free retargeting, and adapts via reinforcement learning.
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TACT-ful: Multi-Channel Terrain Affordance and Compliance Training for Payload-Robust Perceptive Humanoid Locomotion
A multi-channel terrain affordance reward combined with lower-body compliance training via virtual wrenches enables end-to-end PPO-trained humanoid policies to walk at 1 m/s on 0.2 m risers with improved payload robustness.