Papers Humanoid Control
“Humanoid Control” 태그가 달린 논문 37편 · 필터 해제
KungfuBot: Physics-Based Humanoid Whole-Body Control for Learning Highly-Dynamic Skills
Humanoid robots are promising to acquire various skills by imitating human behaviors. However, existing algorithms are only capable of tracking smooth, low-speed human motions, even with delicate reward and curriculum de…
Humanoid ControlRL from Physical Feedback: Aligning Large Motion Models with Humanoid Control
This paper focuses on a critical challenge in robotics: translating text-driven human motions into executable actions for humanoid robots, enabling efficient and cost-effective learning of new behaviors. While existing t…
Humanoid ControlMotion GenerationSemantic correspondenceSkillBlender: Towards Versatile Humanoid Whole-Body Loco-Manipulation via Skill Blending
Humanoid robots hold significant potential in accomplishing daily tasks across diverse environments thanks to their flexibility and human-like morphology. Recent works have made significant progress in humanoid whole-bod…
Hierarchical Reinforcement LearningHumanoid ControlBigger, Regularized, Categorical: High-Capacity Value Functions are Efficient Multi-Task Learners
Recent advances in language modeling and vision stem from training large models on diverse, multi-task data. This paradigm has had limited impact in value-based reinforcement learning (RL), where improvements are often d…
Humanoid ControlLanguage ModelingLanguage ModellingReinforcement Learning (RL)FastTD3: Simple, Fast, and Capable Reinforcement Learning for Humanoid Control
Reinforcement learning (RL) has driven significant progress in robotics, but its complexity and long training times remain major bottlenecks. In this report, we introduce FastTD3, a simple, fast, and capable RL algorithm…
GPUHumanoid ControlMuJoCoReinforcement Learning (RL)One Policy but Many Worlds: A Scalable Unified Policy for Versatile Humanoid Locomotion
Humanoid locomotion faces a critical scalability challenge: traditional reinforcement learning (RL) methods require task-specific rewards and struggle to leverage growing datasets, even as more training terrains are intr…
Humanoid ControlMotion SynthesisReinforcement Learning (RL)HuB: Learning Extreme Humanoid Balance
The human body demonstrates exceptional motor capabilities-such as standing steadily on one foot or performing a high kick with the leg raised over 1.5 meters-both requiring precise balance control. While recent research…
Humanoid ControlVisual Imitation Enables Contextual Humanoid Control
How can we teach humanoids to climb staircases and sit on chairs using the surrounding environment context? Arguably, the simplest way is to just show them-casually capture a human motion video and feed it to humanoids. …
Humanoid ControlZero-Shot Whole-Body Humanoid Control via Behavioral Foundation Models
Unsupervised reinforcement learning (RL) aims at pre-training agents that can solve a wide range of downstream tasks in complex environments. Despite recent advancements, existing approaches suffer from several limitatio…
Humanoid ControlReinforcement Learning (RL)Unsupervised Reinforcement LearningZero-shot GeneralizationReinforcement learning-based motion imitation for physiologically plausible musculoskeletal motor control
How do humans move? The quest to understand human motion has broad applications in numerous fields, ranging from computer animation and motion synthesis to neuroscience, human prosthetics and rehabilitation. Although adv…
Humanoid ControlMotion SynthesisReinforcement Learning (RL)Humanoid-VLA: Towards Universal Humanoid Control with Visual Integration
This paper addresses the limitations of current humanoid robot control frameworks, which primarily rely on reactive mechanisms and lack autonomous interaction capabilities due to data scarcity. We propose Humanoid-VLA, a…
Data AugmentationHumanoid ControlMotion GenerationSafe Bayesian Optimization for the Control of High-Dimensional Embodied Systems
Learning to move is a primary goal for animals and robots, where ensuring safety is often important when optimizing control policies on the embodied systems. For complex tasks such as the control of human or humanoid con…
Bayesian OptimizationHumanoid ControlSafe ExplorationLearning from Massive Human Videos for Universal Humanoid Pose Control
Scalable learning of humanoid robots is crucial for their deployment in real-world applications. While traditional approaches primarily rely on reinforcement learning or teleoperation to achieve whole-body control, they …
Caption GenerationHumanoid Controlmotion retargetingCoarse-to-fine Q-Network with Action Sequence for Data-Efficient Robot Learning
Predicting a sequence of actions has been crucial in the success of recent behavior cloning algorithms in robotics. Can similar ideas improve reinforcement learning (RL)? We answer affirmatively by observing that incorpo…
Humanoid Controlreinforcement-learningReinforcement LearningReinforcement Learning (RL)The Role of Domain Randomization in Training Diffusion Policies for Whole-Body Humanoid Control
Humanoids have the potential to be the ideal embodiment in environments designed for humans. Thanks to the structural similarity to the human body, they benefit from rich sources of demonstration data, e.g., collected vi…
DiversityHumanoid ControlMuJoCo MPC for Humanoid Control: Evaluation on HumanoidBench
We tackle the recently introduced benchmark for whole-body humanoid control HumanoidBench using MuJoCo MPC. We find that sparse reward functions of HumanoidBench yield undesirable and unrealistic behaviors when optimized…
Humanoid ControlMuJoCoCooHOI: Learning Cooperative Human-Object Interaction with Manipulated Object Dynamics
Enabling humanoid robots to clean rooms has long been a pursued dream within humanoid research communities. However, many tasks require multi-humanoid collaboration, such as carrying large and heavy furniture together. G…
Human-Object Interaction DetectionHumanoid ControlImitation LearningObjectHierarchical World Models as Visual Whole-Body Humanoid Controllers
Whole-body control for humanoids is challenging due to the high-dimensional nature of the problem, coupled with the inherent instability of a bipedal morphology. Learning from visual observations further exacerbates this…
Humanoid ControlReal-Time Simulated Avatar from Head-Mounted Sensors
We present SimXR, a method for controlling a simulated avatar from information (headset pose and cameras) obtained from AR / VR headsets. Due to the challenging viewpoint of head-mounted cameras, the human body is often …
Egocentric Pose EstimationHumanoid ControlPose EstimationHumanoid Locomotion as Next Token Prediction
We cast real-world humanoid control as a next token prediction problem, akin to predicting the next word in language. Our model is a causal transformer trained via autoregressive prediction of sensorimotor trajectories. …
Humanoid ControlPrediction