#Motion Generation (11)

Waterfall Pagination

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PKU Motion-Omni End-to-End Dialogue System

@aigclink · 196 views · 2026-09-07

Embodied AIDigital HumanMotion Generation

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ActionPlan: Future-Aware Streaming Motion Synthesis via Frame-Level Action Planning

@XianghuiXie · 182 views · 2026-03-21

Motion GenerationMotion DiffusionStreaming Motion

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Unitree G1 runs at up to 3.3 m/s outdoors with obstacle avoidance

@ZappyZappy7 · 221 views · 2026-09-04

UnitreeUnitree G1Humanoid

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NVIDIA Kimodo Integrated into Unreal Engine 5.8 for Text-to-Animation

@ymt3d · 387 views · 2026-09-03

NVIDIAKimodoMotion Generation

PAMoR: Parameterized Affective Motion Generation in Real Time for Humanoid Robots

People read a humanoid robot's motion in social settings not only for the action performed but for the affect conveyed. Motion carrying that affect has so far been generated for human avatars, where style is taken from a reference clip or an emotion word, neither of which can be quantitatively parameterized. We present PAMoR, which turns affect into a measured control parameter: a valence-arousal (V-A) coordinate computed natively on robot kinematics. It is obtained in closed form from postural expansion and movement energy, and these measurements serve directly as generation conditions, with no human annotation. An action prior and two affect priors, trained in a shared latent space, are composed at each denoising step: the action prior fixes what is performed, the affect priors modulate how. Whole-body motion rolls out autoregressively on a 29-DoF Unitree G1 in real time, with action and affect both editable. Generated motion tracks the commanded V-A over its full range while text-to-motion fidelity still matches text-only baselines. In a perceptual study, raters identify the commanded emotion on 0.38 of trials, above both baselines and approaching the 0.44 reported for acted human bodies.

Yan Pan, Lingfan Bao, Tianhu PengAug 28, 2026

Unitree G1Humanoid robotsaffective motionAug 28, 2026

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Learning Whole-Body Humanoid Locomotion via Motion Generation and Motion Tracking

Whole-body humanoid locomotion is challenging due to high-dimensional control, morphological instability, and the need for real-time adaptation to various terrains using onboard perception. Directly applying reinforcement learning (RL) with reward shaping to humanoid locomotion often leads to lower-body-dominated behaviors, whereas imitation-based RL can learn more coordinated whole-body skills but is typically limited to replaying reference motions without a mechanism to adapt them online from perception for terrain-aware locomotion. To address this gap, we propose a whole-body humanoid locomotion framework that combines skills learned from reference motions with terrain-aware adaptation. We first train a diffusion model on retargeted human motions for real-time prediction of terrain-aware reference motions. Concurrently, we train a whole-body reference tracker with RL using this motion data. To improve robustness under imperfectly generated references, we further fine-tune the tracker with a frozen motion generator in a closed-loop setting. The resulting system supports directional goal-reaching control with terrain-aware whole-body adaptation, and can be deployed on a Unitree G1 humanoid robot with onboard perception and computation. The hardware experiments demonstrate successful traversal over boxes, hurdles, stairs, and mixed terrain combinations. Quantitative results further show the benefits of incorporating online motion generation and fine-tuning the motion tracker for improved generalization and robustness.

Zewei Zhang, Kehan Wen, Michael XuApr 19, 2026

HumanoidWhole-body locomotionMotion GenerationApr 19, 2026

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PINOC T2M vs NVIDIA ARDY Motion Comparison

@Viggle_PINOC · 101 views · 2026-07-27

VigglePINOCARDY

STeP: Signal Temporal Logic for Precise Specifications for Action Generation with Vision Language Models

Vision-language-action (VLA) models have shown impressive generalization, but often lack interpretability and can struggle to follow precise natural language instructions that encode spatial, temporal, and logical requirements. We propose a hierarchical framework that uses Signal Temporal Logic (STL) as a shared representation connecting high-level language understanding with low-level robot execution. A high-level policy leverages a VLM to decompose language instructions into high-level subtasks, generate STL specifications for each subtask, and choose a low-level policy for executing each subtask. The STL specifications translate language-derived intent into precise constraints, and the low-level policy selection determines whether those constraints are enforced directly through STL-guided model-predictive control or monitored during execution of a learned policy for perceptually complex, or contact-rich behaviors. By integrating STL into plan validation, low-level policy, subtask monitoring, and replanning, our framework enables language-derived plans to be checked, optimized, and revised at runtime using a common formal structure. We evaluate the approach on a real-world tabletop domain, demonstrating how formal specifications can improve the precision, reliability, and interpretability of language-conditioned robot planning.

Kasra Torshizi, Anukriti Singh, Sidharth MathurJul 20, 2026

Vision-language modelsTemporal LogicMotion GenerationJul 20, 2026