SceneBot conditions a humanoid tracking policy on motion references and contact labels, using reconstructed scene-interaction data to unify free-space locomotion with contact-rich manipulation and terrain tasks.
mjlab: A lightweight framework for gpu-accelerated robot learn- ing
9 Pith papers cite this work. Polarity classification is still indexing.
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2026 9representative citing papers
TaskNPoint lets humanoid robots learn dynamic skills such as tennis backhands from single short human video demonstrations plus under one hour of single-GPU simulation training, achieving zero-shot generalization to new goal locations without per-task reward tuning.
Predictive Style Matching uses an offline state-conditioned predictor to reduce upper-body style error by an order of magnitude in RL humanoid locomotion while preserving disturbance recovery rates, unlike motion imitation which trades recovery for style.
UniLab is a CPU/GPU heterogeneous system for robot RL training using MuJoCoUni and MotrixSim backends that reports 3-10x end-to-end efficiency improvements and cross-platform compatibility beyond CUDA.
HUSKY combines humanoid-skateboard dynamics modeling with adversarial motion priors and physics-guided lean-to-steer strategies to achieve real-world stable skateboarding on a humanoid robot.
HANDOFF is a distilled mixture-of-experts humanoid whole-body controller that follows a compact task-space interface, matches SOTA velocity tracking, provides large manipulation workspace on Unitree G1, and supports VLM-driven agentic planning with no task-specific data.
MPC-RL combines a centroidal-dynamics MPC reward with a batched GPU solver (π^n MPC) to accelerate RL training for humanoid locomotion and manipulation tasks.
LP-NavOA distills a recurrent local planner into a frozen PPO locomotion backbone to enable obstacle bypassing and goal recovery for humanoids under limited perception, improving simulated on-time arrival from 38-40% to 85-97%.
Web-Gewu delivers a scalable browser-accessible playground for robot reinforcement learning by offloading simulation and training to edge nodes while using the cloud only as a signaling relay for low-latency P2P streaming.
citing papers explorer
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SceneBot: Contact-Prompted General Humanoid Whole Body Tracking with Scene-Interaction
SceneBot conditions a humanoid tracking policy on motion references and contact labels, using reconstructed scene-interaction data to unify free-space locomotion with contact-rich manipulation and terrain tasks.
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TaskNPoint: How to Teach Your Humanoid to Hit a Backhand in Minutes
TaskNPoint lets humanoid robots learn dynamic skills such as tennis backhands from single short human video demonstrations plus under one hour of single-GPU simulation training, achieving zero-shot generalization to new goal locations without per-task reward tuning.
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Predictive Style Matching: Natural and Robust Humanoid Locomotion
Predictive Style Matching uses an offline state-conditioned predictor to reduce upper-body style error by an order of magnitude in RL humanoid locomotion while preserving disturbance recovery rates, unlike motion imitation which trades recovery for style.
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UniLab: A Heterogeneous Architecture for Robot RL Beyond GPU-Dominant Paradigms
UniLab is a CPU/GPU heterogeneous system for robot RL training using MuJoCoUni and MotrixSim backends that reports 3-10x end-to-end efficiency improvements and cross-platform compatibility beyond CUDA.
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HUSKY: Humanoid Skateboarding System via Physics-Aware Whole-Body Control
HUSKY combines humanoid-skateboard dynamics modeling with adversarial motion priors and physics-guided lean-to-steer strategies to achieve real-world stable skateboarding on a humanoid robot.
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HANDOFF: Humanoid Agentic Task-Space Whole-Body Control via Distilled Complementary Teachers
HANDOFF is a distilled mixture-of-experts humanoid whole-body controller that follows a compact task-space interface, matches SOTA velocity tracking, provides large manipulation workspace on Unitree G1, and supports VLM-driven agentic planning with no task-specific data.
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Accelerating and Scaling MPC-Guided Reinforcement Learning for Humanoid Locomotion and Manipulation
MPC-RL combines a centroidal-dynamics MPC reward with a batched GPU solver (π^n MPC) to accelerate RL training for humanoid locomotion and manipulation tasks.
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LP-NavOA: Integrated Local Navigation and Obstacle Avoidance for Humanoid Robots under Limited Perception
LP-NavOA distills a recurrent local planner into a frozen PPO locomotion backbone to enable obstacle bypassing and goal recovery for humanoids under limited perception, improving simulated on-time arrival from 38-40% to 85-97%.
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Web-Gewu: A Browser-Based Interactive Playground for Robot Reinforcement Learning
Web-Gewu delivers a scalable browser-accessible playground for robot reinforcement learning by offloading simulation and training to edge nodes while using the cloud only as a signaling relay for low-latency P2P streaming.